Improvise a Jazz Solo with an LSTM Network

Welcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assignment.

You will learn to:

• Apply an LSTM to music generation.
• Generate your own jazz music with deep learning.

Please run the following cell to load all the packages required in this assignment. This may take a few minutes.

from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import * 
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K

Using TensorFlow backend.

1 - Problem statement

You would like to create a jazz music piece specially for a friend's birthday. However, you don't know any instruments or music composition. Fortunately, you know deep learning and will solve this problem using an LSTM netwok.

You will train a network to generate novel jazz solos in a style representative of a body of performed work.

1.1 - Dataset

You will train your algorithm on a corpus of Jazz music. Run the cell below to listen to a snippet of the audio from the training set:

IPython.display.Audio('./data/30s_seq.mp3')

We have taken care of the preprocessing of the musical data to render it in terms of musical "values." You can informally think of each "value" as a note, which comprises a pitch and a duration. For example, if you press down a specific piano key for 0.5 seconds, then you have just played a note. In music theory, a "value" is actually more complicated than this--specifically, it also captures the information needed to play multiple notes at the same time. For example, when playing a music piece, you might press down two piano keys at the same time (playng multiple notes at the same time generates what's called a "chord"). But we don't need to worry about the details of music theory for this assignment. For the purpose of this assignment, all you need to know is that we will obtain a dataset of values, and will learn an RNN model to generate sequences of values.

Our music generation system will use 78 unique values. Run the following code to load the raw music data and preprocess it into values. This might take a few minutes.

X, Y, n_values, indices_values = load_music_utils()
print('shape of X:', X.shape)
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('Shape of Y:', Y.shape)

shape of X: (60, 30, 78)
number of training examples: 60
Tx (length of sequence): 30
total # of unique values: 78
Shape of Y: (30, 60, 78)

You have just loaded the following:

• X: This is an (m, $T_x$, 78) dimensional array. We have m training examples, each of which is a snippet of $T_x =30$ musical values. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t.

• Y: This is essentially the same as X, but shifted one step to the left (to the past). Similar to the dinosaurus assignment, we're interested in the network using the previous values to predict the next value, so our sequence model will try to predict $y^{\langle t \rangle}$ given $x^{\langle 1\rangle}, \ldots, x^{\langle t \rangle}$. However, the data in Y is reordered to be dimension $(T_y, m, 78)$, where $T_y = T_x$. This format makes it more convenient to feed to the LSTM later.

• n_values: The number of unique values in this dataset. This should be 78.

• indices_values: python dictionary mapping from 0-77 to musical values.

1.2 - Overview of our model

Here is the architecture of the model we will use. This is similar to the Dinosaurus model you had used in the previous notebook, except that in you will be implementing it in Keras. The architecture is as follows:

We will be training the model on random snippets of 30 values taken from a much longer piece of music. Thus, we won't bother to set the first input $x^{\langle 1 \rangle} = \vec{0}$, which we had done previously to denote the start of a dinosaur name, since now most of these snippets of audio start somewhere in the middle of a piece of music. We are setting each of the snippts to have the same length $T_x = 30$ to make vectorization easier.

2 - Building the model

In this part you will build and train a model that will learn musical patterns. To do so, you will need to build a model that takes in X of shape $(m, T_x, 78)$ and Y of shape $(T_y, m, 78)$. We will use an LSTM with 64 dimensional hidden states. Lets set n_a = 64.

n_a = 64 

Here's how you can create a Keras model with multiple inputs and outputs. If you're building an RNN where even at test time entire input sequence $x^{\langle 1 \rangle}, x^{\langle 2 \rangle}, \ldots, x^{\langle T_x \rangle}$ were given in advance, for example if the inputs were words and the output was a label, then Keras has simple built-in functions to build the model. However, for sequence generation, at test time we don't know all the values of $x^{\langle t\rangle}$ in advance; instead we generate them one at a time using $x^{\langle t\rangle} = y^{\langle t-1 \rangle}$. So the code will be a bit more complicated, and you'll need to implement your own for-loop to iterate over the different time steps.

The function djmodel() will call the LSTM layer $T_x$ times using a for-loop, and it is important that all $T_x$ copies have the same weights. I.e., it should not re-initiaiize the weights every time---the $T_x$ steps should have shared weights. The key steps for implementing layers with shareable weights in Keras are:

1. Define the layer objects (we will use global variables for this).
2. Call these objects when propagating the input.

We have defined the layers objects you need as global variables. Please run the next cell to create them. Please check the Keras documentation to make sure you understand what these layers are: Reshape(), LSTM(), Dense().

reshapor = Reshape((1, 78))                        # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True)         # Used in Step 2.C
densor = Dense(n_values, activation='softmax')     # Used in Step 2.D

Each of reshapor, LSTM_cell and densor are now layer objects, and you can use them to implement djmodel(). In order to propagate a Keras tensor object X through one of these layers, use layer_object(X) (or layer_object([X,Y]) if it requires multiple inputs.). For example, reshapor(X) will propagate X through the Reshape((1,78)) layer defined above.

Exercise: Implement djmodel(). You will need to carry out 2 steps:

1. Create an empty list "outputs" to save the outputs of the LSTM Cell at every time step.

2. Loop for $t \in 1, \ldots, T_x$:

   A. Select the "t"th time-step vector from X. The shape of this selection should be (78,). To do so, create a custom Lambda layer in Keras by using this line of code:

           x = Lambda(lambda x: X[:,t,:])(X)

   Look over the Keras documentation to figure out what this does. It is creating a "temporary" or "unnamed" function (that's what Lambda functions are) that extracts out the appropriate one-hot vector, and making this function a Keras Layer object to apply to X.

   B. Reshape x to be (1,78). You may find the reshapor() layer (defined below) helpful.

   C. Run x through one step of LSTM_cell. Remember to initialize the LSTM_cell with the previous step's hidden state $a$ and cell state $c$. Use the following formatting:

   a, _, c = LSTM_cell(input_x, initial_state=[previous hidden state, previous cell state])
   

   D. Propagate the LSTM's output activation value through a dense+softmax layer using densor.

   E. Append the predicted value to the list of "outputs"

# GRADED FUNCTION: djmodel

def djmodel(Tx, n_a, n_values):
    """
    Implement the model
    
    Arguments:
    Tx -- length of the sequence in a corpus
    n_a -- the number of activations used in our model
    n_values -- number of unique values in the music data 
    
    Returns:
    model -- a keras model with the 
    """
    
    # Define the input of your model with a shape 
    X = Input(shape=(Tx, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    
    ### START CODE HERE ### 
    # Step 1: Create empty list to append the outputs while you iterate (≈1 line)
    outputs = []
    
    # Step 2: Loop
    for t in range(Tx):
        
        # Step 2.A: select the "t"th time step vector from X. 
        x = Lambda(lambda x: X[:,t,:])(X)
        # Step 2.B: Use reshapor to reshape x to be (1, n_values) (≈1 line)
        x = reshapor(x)
        # Step 2.C: Perform one step of the LSTM_cell
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        # Step 2.D: Apply densor to the hidden state output of LSTM_Cell
        out = densor(a)
        # Step 2.E: add the output to "outputs"
        outputs.append(out)
        
    # Step 3: Create model instance
    model = Model([X, a0, c0], outputs)
    
    ### END CODE HERE ###
    
    return model

Run the following cell to define your model. We will use Tx=30, n_a=64 (the dimension of the LSTM activations), and n_values=78. This cell may take a few seconds to run.

model = djmodel(Tx = 30 , n_a = 64, n_values = 78)

You now need to compile your model to be trained. We will Adam and a categorical cross-entropy loss.

opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.01)

model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

Finally, lets initialize a0 and c0 for the LSTM's initial state to be zero.

m = 60
a0 = np.zeros((m, n_a))
c0 = np.zeros((m, n_a))

Lets now fit the model! We will turn Y to a list before doing so, since the cost function expects Y to be provided in this format (one list item per time-step). So list(Y) is a list with 30 items, where each of the list items is of shape (60,78). Lets train for 100 epochs. This will take a few minutes.

model.fit([X, a0, c0], list(Y), epochs=100)

Epoch 1/100
60/60 [==============================] - 5s - loss: 125.9040 - dense_1_loss_1: 4.3549 - dense_1_loss_2: 4.3491 - dense_1_loss_3: 4.3446 - dense_1_loss_4: 4.3425 - dense_1_loss_5: 4.3457 - dense_1_loss_6: 4.3444 - dense_1_loss_7: 4.3403 - dense_1_loss_8: 4.3426 - dense_1_loss_9: 4.3420 - dense_1_loss_10: 4.3363 - dense_1_loss_11: 4.3399 - dense_1_loss_12: 4.3542 - dense_1_loss_13: 4.3405 - dense_1_loss_14: 4.3315 - dense_1_loss_15: 4.3349 - dense_1_loss_16: 4.3365 - dense_1_loss_17: 4.3502 - dense_1_loss_18: 4.3426 - dense_1_loss_19: 4.3398 - dense_1_loss_20: 4.3456 - dense_1_loss_21: 4.3334 - dense_1_loss_22: 4.3307 - dense_1_loss_23: 4.3370 - dense_1_loss_24: 4.3398 - dense_1_loss_25: 4.3419 - dense_1_loss_26: 4.3336 - dense_1_loss_27: 4.3417 - dense_1_loss_28: 4.3389 - dense_1_loss_29: 4.3489 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0000e+00 - dense_1_acc_2: 0.0000e+00 - dense_1_acc_3: 0.0333 - dense_1_acc_4: 0.0167 - dense_1_acc_5: 0.0500 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.0833 - dense_1_acc_8: 0.0667 - dense_1_acc_9: 0.0833 - dense_1_acc_10: 0.0833 - dense_1_acc_11: 0.1000 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.0333 - dense_1_acc_14: 0.0833 - dense_1_acc_15: 0.1000 - dense_1_acc_16: 0.0833 - dense_1_acc_17: 0.0333 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1000 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.0833 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.0833 - dense_1_acc_27: 0.0167 - dense_1_acc_28: 0.1000 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00                                                                             
Epoch 2/100
60/60 [==============================] - 0s - loss: 122.6568 - dense_1_loss_1: 4.3326 - dense_1_loss_2: 4.3046 - dense_1_loss_3: 4.2773 - dense_1_loss_4: 4.2703 - dense_1_loss_5: 4.2588 - dense_1_loss_6: 4.2670 - dense_1_loss_7: 4.2432 - dense_1_loss_8: 4.2369 - dense_1_loss_9: 4.2453 - dense_1_loss_10: 4.2128 - dense_1_loss_11: 4.2108 - dense_1_loss_12: 4.2624 - dense_1_loss_13: 4.2184 - dense_1_loss_14: 4.2120 - dense_1_loss_15: 4.2054 - dense_1_loss_16: 4.2141 - dense_1_loss_17: 4.2386 - dense_1_loss_18: 4.2264 - dense_1_loss_19: 4.1898 - dense_1_loss_20: 4.2158 - dense_1_loss_21: 4.1903 - dense_1_loss_22: 4.1631 - dense_1_loss_23: 4.2017 - dense_1_loss_24: 4.2174 - dense_1_loss_25: 4.2308 - dense_1_loss_26: 4.1616 - dense_1_loss_27: 4.2144 - dense_1_loss_28: 4.2066 - dense_1_loss_29: 4.2285 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0500 - dense_1_acc_3: 0.1333 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.2167 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.0833 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.1500 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.1167 - dense_1_acc_17: 0.1000 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.2333 - dense_1_acc_27: 0.0667 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00     
Epoch 3/100
60/60 [==============================] - 0s - loss: 117.0733 - dense_1_loss_1: 4.3111 - dense_1_loss_2: 4.2544 - dense_1_loss_3: 4.1883 - dense_1_loss_4: 4.1658 - dense_1_loss_5: 4.1323 - dense_1_loss_6: 4.1614 - dense_1_loss_7: 4.0850 - dense_1_loss_8: 4.0316 - dense_1_loss_9: 4.0027 - dense_1_loss_10: 3.8873 - dense_1_loss_11: 3.8779 - dense_1_loss_12: 4.1530 - dense_1_loss_13: 3.9607 - dense_1_loss_14: 3.9360 - dense_1_loss_15: 3.9995 - dense_1_loss_16: 4.0009 - dense_1_loss_17: 4.1242 - dense_1_loss_18: 4.1254 - dense_1_loss_19: 3.8774 - dense_1_loss_20: 4.0043 - dense_1_loss_21: 3.9801 - dense_1_loss_22: 3.8202 - dense_1_loss_23: 3.8966 - dense_1_loss_24: 3.9966 - dense_1_loss_25: 4.1391 - dense_1_loss_26: 3.7823 - dense_1_loss_27: 4.0445 - dense_1_loss_28: 3.9607 - dense_1_loss_29: 4.1739 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1667 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.1667 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1500 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.0833 - dense_1_acc_14: 0.1000 - dense_1_acc_15: 0.0667 - dense_1_acc_16: 0.0667 - dense_1_acc_17: 0.0333 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0500 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.0667 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.0500 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0333 - dense_1_acc_29: 0.0167 - dense_1_acc_30: 0.0000e+00         
Epoch 4/100
60/60 [==============================] - 0s - loss: 112.9154 - dense_1_loss_1: 4.2889 - dense_1_loss_2: 4.2037 - dense_1_loss_3: 4.0946 - dense_1_loss_4: 4.0680 - dense_1_loss_5: 3.9878 - dense_1_loss_6: 4.0213 - dense_1_loss_7: 3.9193 - dense_1_loss_8: 3.7682 - dense_1_loss_9: 3.8235 - dense_1_loss_10: 3.6754 - dense_1_loss_11: 3.7804 - dense_1_loss_12: 4.0816 - dense_1_loss_13: 3.8364 - dense_1_loss_14: 3.7726 - dense_1_loss_15: 3.8076 - dense_1_loss_16: 3.8271 - dense_1_loss_17: 3.9546 - dense_1_loss_18: 3.8845 - dense_1_loss_19: 3.7412 - dense_1_loss_20: 3.9397 - dense_1_loss_21: 3.8654 - dense_1_loss_22: 3.7902 - dense_1_loss_23: 3.7795 - dense_1_loss_24: 3.7457 - dense_1_loss_25: 3.9795 - dense_1_loss_26: 3.6115 - dense_1_loss_27: 3.7478 - dense_1_loss_28: 3.8803 - dense_1_loss_29: 4.0392 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1500 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.0500 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.0667 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1167 - dense_1_acc_15: 0.1333 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0667 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1167 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.1333 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00         
Epoch 5/100
60/60 [==============================] - 0s - loss: 110.2597 - dense_1_loss_1: 4.2705 - dense_1_loss_2: 4.1568 - dense_1_loss_3: 4.0205 - dense_1_loss_4: 4.0005 - dense_1_loss_5: 3.8821 - dense_1_loss_6: 3.9264 - dense_1_loss_7: 3.8413 - dense_1_loss_8: 3.6637 - dense_1_loss_9: 3.7376 - dense_1_loss_10: 3.5831 - dense_1_loss_11: 3.7226 - dense_1_loss_12: 4.0011 - dense_1_loss_13: 3.7122 - dense_1_loss_14: 3.6388 - dense_1_loss_15: 3.7068 - dense_1_loss_16: 3.7367 - dense_1_loss_17: 3.7956 - dense_1_loss_18: 3.7533 - dense_1_loss_19: 3.6622 - dense_1_loss_20: 3.9071 - dense_1_loss_21: 3.7816 - dense_1_loss_22: 3.6928 - dense_1_loss_23: 3.6583 - dense_1_loss_24: 3.6317 - dense_1_loss_25: 3.9247 - dense_1_loss_26: 3.5430 - dense_1_loss_27: 3.6524 - dense_1_loss_28: 3.7684 - dense_1_loss_29: 3.8877 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1667 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1667 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1000 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1000 - dense_1_acc_12: 0.0500 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.0500 - dense_1_acc_19: 0.1667 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.0167 - dense_1_acc_26: 0.1667 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.1000 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00         
Epoch 6/100
60/60 [==============================] - 0s - loss: 107.9783 - dense_1_loss_1: 4.2532 - dense_1_loss_2: 4.1154 - dense_1_loss_3: 3.9407 - dense_1_loss_4: 3.9188 - dense_1_loss_5: 3.7855 - dense_1_loss_6: 3.8511 - dense_1_loss_7: 3.7890 - dense_1_loss_8: 3.5661 - dense_1_loss_9: 3.6302 - dense_1_loss_10: 3.4763 - dense_1_loss_11: 3.6447 - dense_1_loss_12: 3.8714 - dense_1_loss_13: 3.5687 - dense_1_loss_14: 3.4623 - dense_1_loss_15: 3.6039 - dense_1_loss_16: 3.6463 - dense_1_loss_17: 3.6529 - dense_1_loss_18: 3.7222 - dense_1_loss_19: 3.5496 - dense_1_loss_20: 3.8246 - dense_1_loss_21: 3.7585 - dense_1_loss_22: 3.6430 - dense_1_loss_23: 3.5896 - dense_1_loss_24: 3.6176 - dense_1_loss_25: 3.9002 - dense_1_loss_26: 3.4826 - dense_1_loss_27: 3.6797 - dense_1_loss_28: 3.6516 - dense_1_loss_29: 3.7825 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.1333 - dense_1_acc_9: 0.1667 - dense_1_acc_10: 0.1833 - dense_1_acc_11: 0.1333 - dense_1_acc_12: 0.0833 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1833 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.1167 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.1167 - dense_1_acc_25: 0.0000e+00 - dense_1_acc_26: 0.1667 - dense_1_acc_27: 0.0667 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00     
Epoch 7/100
60/60 [==============================] - 0s - loss: 104.8643 - dense_1_loss_1: 4.2384 - dense_1_loss_2: 4.0770 - dense_1_loss_3: 3.8699 - dense_1_loss_4: 3.8504 - dense_1_loss_5: 3.6831 - dense_1_loss_6: 3.7806 - dense_1_loss_7: 3.7355 - dense_1_loss_8: 3.4662 - dense_1_loss_9: 3.5230 - dense_1_loss_10: 3.3787 - dense_1_loss_11: 3.5172 - dense_1_loss_12: 3.7278 - dense_1_loss_13: 3.4227 - dense_1_loss_14: 3.2936 - dense_1_loss_15: 3.4729 - dense_1_loss_16: 3.5240 - dense_1_loss_17: 3.5398 - dense_1_loss_18: 3.5894 - dense_1_loss_19: 3.4475 - dense_1_loss_20: 3.7086 - dense_1_loss_21: 3.7033 - dense_1_loss_22: 3.5503 - dense_1_loss_23: 3.4632 - dense_1_loss_24: 3.5312 - dense_1_loss_25: 3.7995 - dense_1_loss_26: 3.3369 - dense_1_loss_27: 3.4836 - dense_1_loss_28: 3.5284 - dense_1_loss_29: 3.6214 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.2000 - dense_1_acc_9: 0.1833 - dense_1_acc_10: 0.1833 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.1833 - dense_1_acc_14: 0.2167 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.2167 - dense_1_acc_19: 0.1667 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.1333 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.3667 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00     
Epoch 8/100
60/60 [==============================] - 0s - loss: 101.2794 - dense_1_loss_1: 4.2249 - dense_1_loss_2: 4.0353 - dense_1_loss_3: 3.8011 - dense_1_loss_4: 3.7676 - dense_1_loss_5: 3.5933 - dense_1_loss_6: 3.6910 - dense_1_loss_7: 3.6350 - dense_1_loss_8: 3.3723 - dense_1_loss_9: 3.4118 - dense_1_loss_10: 3.2261 - dense_1_loss_11: 3.3967 - dense_1_loss_12: 3.5866 - dense_1_loss_13: 3.2628 - dense_1_loss_14: 3.1666 - dense_1_loss_15: 3.3527 - dense_1_loss_16: 3.3848 - dense_1_loss_17: 3.3601 - dense_1_loss_18: 3.4350 - dense_1_loss_19: 3.2700 - dense_1_loss_20: 3.5765 - dense_1_loss_21: 3.5145 - dense_1_loss_22: 3.3161 - dense_1_loss_23: 3.3409 - dense_1_loss_24: 3.3609 - dense_1_loss_25: 3.6265 - dense_1_loss_26: 3.2130 - dense_1_loss_27: 3.4337 - dense_1_loss_28: 3.3773 - dense_1_loss_29: 3.5461 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.2000 - dense_1_acc_10: 0.2333 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.2000 - dense_1_acc_14: 0.2333 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1833 - dense_1_acc_19: 0.2333 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.1333 - dense_1_acc_22: 0.2000 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.1667 - dense_1_acc_25: 0.1500 - dense_1_acc_26: 0.2667 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.2333 - dense_1_acc_29: 0.1000 - dense_1_acc_30: 0.0000e+00     
Epoch 9/100
60/60 [==============================] - 0s - loss: 97.5204 - dense_1_loss_1: 4.2132 - dense_1_loss_2: 3.9938 - dense_1_loss_3: 3.7228 - dense_1_loss_4: 3.6869 - dense_1_loss_5: 3.4899 - dense_1_loss_6: 3.5940 - dense_1_loss_7: 3.5328 - dense_1_loss_8: 3.2687 - dense_1_loss_9: 3.2599 - dense_1_loss_10: 3.0860 - dense_1_loss_11: 3.2446 - dense_1_loss_12: 3.4448 - dense_1_loss_13: 3.0723 - dense_1_loss_14: 2.9683 - dense_1_loss_15: 3.1960 - dense_1_loss_16: 3.2598 - dense_1_loss_17: 3.2428 - dense_1_loss_18: 3.2547 - dense_1_loss_19: 3.1466 - dense_1_loss_20: 3.4448 - dense_1_loss_21: 3.3736 - dense_1_loss_22: 3.1863 - dense_1_loss_23: 3.2743 - dense_1_loss_24: 3.2657 - dense_1_loss_25: 3.4542 - dense_1_loss_26: 3.0484 - dense_1_loss_27: 3.2197 - dense_1_loss_28: 3.1705 - dense_1_loss_29: 3.4052 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.2000 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.2333 - dense_1_acc_10: 0.2833 - dense_1_acc_11: 0.2000 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.2500 - dense_1_acc_14: 0.2833 - dense_1_acc_15: 0.2333 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2167 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.2167 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1167 - dense_1_acc_28: 0.2333 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00     
Epoch 10/100
60/60 [==============================] - 0s - loss: 93.2951 - dense_1_loss_1: 4.2026 - dense_1_loss_2: 3.9532 - dense_1_loss_3: 3.6438 - dense_1_loss_4: 3.5973 - dense_1_loss_5: 3.3823 - dense_1_loss_6: 3.4690 - dense_1_loss_7: 3.4083 - dense_1_loss_8: 3.1418 - dense_1_loss_9: 3.1179 - dense_1_loss_10: 2.8840 - dense_1_loss_11: 3.0981 - dense_1_loss_12: 3.2677 - dense_1_loss_13: 2.9144 - dense_1_loss_14: 2.8569 - dense_1_loss_15: 3.0905 - dense_1_loss_16: 3.1065 - dense_1_loss_17: 3.0565 - dense_1_loss_18: 3.0391 - dense_1_loss_19: 3.0159 - dense_1_loss_20: 3.2457 - dense_1_loss_21: 3.1609 - dense_1_loss_22: 2.9807 - dense_1_loss_23: 3.0404 - dense_1_loss_24: 3.0801 - dense_1_loss_25: 3.3126 - dense_1_loss_26: 2.8343 - dense_1_loss_27: 3.1310 - dense_1_loss_28: 2.9972 - dense_1_loss_29: 3.2668 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1000 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1833 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.2333 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.2333 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.1833 - dense_1_acc_19: 0.2333 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1833 - dense_1_acc_23: 0.1667 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.2833 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00     
Epoch 11/100
60/60 [==============================] - 0s - loss: 89.2546 - dense_1_loss_1: 4.1930 - dense_1_loss_2: 3.9154 - dense_1_loss_3: 3.5726 - dense_1_loss_4: 3.5090 - dense_1_loss_5: 3.2842 - dense_1_loss_6: 3.3255 - dense_1_loss_7: 3.2574 - dense_1_loss_8: 3.0075 - dense_1_loss_9: 2.9958 - dense_1_loss_10: 2.7867 - dense_1_loss_11: 2.9262 - dense_1_loss_12: 3.1098 - dense_1_loss_13: 2.7767 - dense_1_loss_14: 2.7497 - dense_1_loss_15: 2.8977 - dense_1_loss_16: 2.9300 - dense_1_loss_17: 2.9385 - dense_1_loss_18: 2.8804 - dense_1_loss_19: 2.9554 - dense_1_loss_20: 3.1472 - dense_1_loss_21: 3.0215 - dense_1_loss_22: 2.8213 - dense_1_loss_23: 2.8513 - dense_1_loss_24: 2.9052 - dense_1_loss_25: 3.1611 - dense_1_loss_26: 2.6129 - dense_1_loss_27: 2.8965 - dense_1_loss_28: 2.7634 - dense_1_loss_29: 3.0626 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1833 - dense_1_acc_8: 0.1667 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.2667 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.2167 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.2500 - dense_1_acc_16: 0.1833 - dense_1_acc_17: 0.2667 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2000 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.1667 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.2000 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.2000 - dense_1_acc_28: 0.2667 - dense_1_acc_29: 0.1000 - dense_1_acc_30: 0.0000e+00     
Epoch 12/100
60/60 [==============================] - 0s - loss: 85.1477 - dense_1_loss_1: 4.1850 - dense_1_loss_2: 3.8815 - dense_1_loss_3: 3.4970 - dense_1_loss_4: 3.4241 - dense_1_loss_5: 3.1747 - dense_1_loss_6: 3.1674 - dense_1_loss_7: 3.1523 - dense_1_loss_8: 2.9025 - dense_1_loss_9: 2.8640 - dense_1_loss_10: 2.6380 - dense_1_loss_11: 2.7936 - dense_1_loss_12: 2.9178 - dense_1_loss_13: 2.6395 - dense_1_loss_14: 2.5885 - dense_1_loss_15: 2.8020 - dense_1_loss_16: 2.8228 - dense_1_loss_17: 2.7294 - dense_1_loss_18: 2.7477 - dense_1_loss_19: 2.7194 - dense_1_loss_20: 2.9086 - dense_1_loss_21: 2.7878 - dense_1_loss_22: 2.6447 - dense_1_loss_23: 2.6568 - dense_1_loss_24: 2.7479 - dense_1_loss_25: 2.9697 - dense_1_loss_26: 2.4406 - dense_1_loss_27: 2.8676 - dense_1_loss_28: 2.6169 - dense_1_loss_29: 2.8599 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.2000 - dense_1_acc_8: 0.1833 - dense_1_acc_9: 0.2500 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.1833 - dense_1_acc_12: 0.1667 - dense_1_acc_13: 0.2500 - dense_1_acc_14: 0.2500 - dense_1_acc_15: 0.2833 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.2500 - dense_1_acc_18: 0.2333 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.1667 - dense_1_acc_21: 0.2500 - dense_1_acc_22: 0.2167 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.1500 - dense_1_acc_25: 0.1000 - dense_1_acc_26: 0.3167 - dense_1_acc_27: 0.1500 - dense_1_acc_28: 0.2500 - dense_1_acc_29: 0.1667 - dense_1_acc_30: 0.0000e+00     
Epoch 13/100
60/60 [==============================] - 0s - loss: 81.0322 - dense_1_loss_1: 4.1763 - dense_1_loss_2: 3.8493 - dense_1_loss_3: 3.4276 - dense_1_loss_4: 3.3310 - dense_1_loss_5: 3.0635 - dense_1_loss_6: 3.0125 - dense_1_loss_7: 3.0074 - dense_1_loss_8: 2.7820 - dense_1_loss_9: 2.6978 - dense_1_loss_10: 2.5079 - dense_1_loss_11: 2.6490 - dense_1_loss_12: 2.7379 - dense_1_loss_13: 2.4823 - dense_1_loss_14: 2.4618 - dense_1_loss_15: 2.5889 - dense_1_loss_16: 2.6738 - dense_1_loss_17: 2.6001 - dense_1_loss_18: 2.5553 - dense_1_loss_19: 2.6133 - dense_1_loss_20: 2.7357 - dense_1_loss_21: 2.5915 - dense_1_loss_22: 2.5230 - dense_1_loss_23: 2.4973 - dense_1_loss_24: 2.5586 - dense_1_loss_25: 2.7808 - dense_1_loss_26: 2.3086 - dense_1_loss_27: 2.6981 - dense_1_loss_28: 2.4811 - dense_1_loss_29: 2.6398 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.1833 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1833 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.3167 - dense_1_acc_10: 0.3333 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.1833 - dense_1_acc_13: 0.2667 - dense_1_acc_14: 0.2667 - dense_1_acc_15: 0.3333 - dense_1_acc_16: 0.2167 - dense_1_acc_17: 0.2667 - dense_1_acc_18: 0.3000 - dense_1_acc_19: 0.2500 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.3000 - dense_1_acc_22: 0.2667 - dense_1_acc_23: 0.2667 - dense_1_acc_24: 0.2000 - dense_1_acc_25: 0.1500 - dense_1_acc_26: 0.3500 - dense_1_acc_27: 0.2167 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00     
Epoch 14/100
60/60 [==============================] - 0s - loss: 77.9601 - dense_1_loss_1: 4.1701 - dense_1_loss_2: 3.8156 - dense_1_loss_3: 3.3568 - dense_1_loss_4: 3.2374 - dense_1_loss_5: 2.9629 - dense_1_loss_6: 2.8653 - dense_1_loss_7: 2.8869 - dense_1_loss_8: 2.6852 - dense_1_loss_9: 2.5626 - dense_1_loss_10: 2.4354 - dense_1_loss_11: 2.5116 - dense_1_loss_12: 2.5677 - dense_1_loss_13: 2.3224 - dense_1_loss_14: 2.3686 - dense_1_loss_15: 2.4150 - dense_1_loss_16: 2.5839 - dense_1_loss_17: 2.4449 - dense_1_loss_18: 2.4903 - dense_1_loss_19: 2.4915 - dense_1_loss_20: 2.6270 - dense_1_loss_21: 2.5091 - dense_1_loss_22: 2.4627 - dense_1_loss_23: 2.3852 - dense_1_loss_24: 2.4574 - dense_1_loss_25: 2.5815 - dense_1_loss_26: 2.3192 - dense_1_loss_27: 2.5726 - dense_1_loss_28: 2.3630 - dense_1_loss_29: 2.5083 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2667 - dense_1_acc_4: 0.2000 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.2167 - dense_1_acc_7: 0.2833 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.3500 - dense_1_acc_10: 0.3167 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.2333 - dense_1_acc_13: 0.3333 - dense_1_acc_14: 0.2833 - dense_1_acc_15: 0.4000 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.3000 - dense_1_acc_18: 0.3333 - dense_1_acc_19: 0.3333 - dense_1_acc_20: 0.3167 - dense_1_acc_21: 0.3167 - dense_1_acc_22: 0.2833 - dense_1_acc_23: 0.3333 - dense_1_acc_24: 0.2333 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.3667 - dense_1_acc_27: 0.3167 - dense_1_acc_28: 0.4000 - dense_1_acc_29: 0.2333 - dense_1_acc_30: 0.0000e+00     
Epoch 15/100
60/60 [==============================] - 0s - loss: 73.6257 - dense_1_loss_1: 4.1631 - dense_1_loss_2: 3.7769 - dense_1_loss_3: 3.2732 - dense_1_loss_4: 3.1412 - dense_1_loss_5: 2.8381 - dense_1_loss_6: 2.7260 - dense_1_loss_7: 2.7418 - dense_1_loss_8: 2.5269 - dense_1_loss_9: 2.5045 - dense_1_loss_10: 2.2739 - dense_1_loss_11: 2.3766 - dense_1_loss_12: 2.4051 - dense_1_loss_13: 2.2745 - dense_1_loss_14: 2.2042 - dense_1_loss_15: 2.2568 - dense_1_loss_16: 2.3940 - dense_1_loss_17: 2.3293 - dense_1_loss_18: 2.2735 - dense_1_loss_19: 2.3449 - dense_1_loss_20: 2.4590 - dense_1_loss_21: 2.2738 - dense_1_loss_22: 2.2730 - dense_1_loss_23: 2.1869 - dense_1_loss_24: 2.2947 - dense_1_loss_25: 2.3850 - dense_1_loss_26: 2.0492 - dense_1_loss_27: 2.3851 - dense_1_loss_28: 2.2298 - dense_1_loss_29: 2.2649 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.2167 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.2667 - dense_1_acc_9: 0.3167 - dense_1_acc_10: 0.3333 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.2500 - dense_1_acc_13: 0.3167 - dense_1_acc_14: 0.3000 - dense_1_acc_15: 0.4167 - dense_1_acc_16: 0.3000 - dense_1_acc_17: 0.2667 - dense_1_acc_18: 0.3833 - dense_1_acc_19: 0.3333 - dense_1_acc_20: 0.3000 - dense_1_acc_21: 0.3667 - dense_1_acc_22: 0.2500 - dense_1_acc_23: 0.3500 - dense_1_acc_24: 0.2667 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.3833 - dense_1_acc_27: 0.3333 - dense_1_acc_28: 0.4000 - dense_1_acc_29: 0.3333 - dense_1_acc_30: 0.0000e+00     
Epoch 16/100
60/60 [==============================] - 0s - loss: 70.3618 - dense_1_loss_1: 4.1553 - dense_1_loss_2: 3.7367 - dense_1_loss_3: 3.1916 - dense_1_loss_4: 3.0392 - dense_1_loss_5: 2.7275 - dense_1_loss_6: 2.5766 - dense_1_loss_7: 2.6109 - dense_1_loss_8: 2.4347 - dense_1_loss_9: 2.3557 - dense_1_loss_10: 2.1732 - dense_1_loss_11: 2.2006 - dense_1_loss_12: 2.2638 - dense_1_loss_13: 2.1105 - dense_1_loss_14: 2.0830 - dense_1_loss_15: 2.1623 - dense_1_loss_16: 2.2633 - dense_1_loss_17: 2.1704 - dense_1_loss_18: 2.0982 - dense_1_loss_19: 2.2236 - dense_1_loss_20: 2.2410 - dense_1_loss_21: 2.1717 - dense_1_loss_22: 2.1792 - dense_1_loss_23: 2.0740 - dense_1_loss_24: 2.2246 - dense_1_loss_25: 2.3523 - dense_1_loss_26: 1.9297 - dense_1_loss_27: 2.3692 - dense_1_loss_28: 2.1127 - dense_1_loss_29: 2.1301 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.3000 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.2833 - dense_1_acc_7: 0.2833 - dense_1_acc_8: 0.3000 - dense_1_acc_9: 0.3667 - dense_1_acc_10: 0.3000 - dense_1_acc_11: 0.3667 - dense_1_acc_12: 0.3167 - dense_1_acc_13: 0.4333 - dense_1_acc_14: 0.3667 - dense_1_acc_15: 0.4000 - dense_1_acc_16: 0.3500 - dense_1_acc_17: 0.3333 - dense_1_acc_18: 0.3833 - dense_1_acc_19: 0.3500 - dense_1_acc_20: 0.3333 - dense_1_acc_21: 0.3500 - dense_1_acc_22: 0.2667 - dense_1_acc_23: 0.3333 - dense_1_acc_24: 0.3000 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.5000 - dense_1_acc_27: 0.3000 - dense_1_acc_28: 0.4167 - dense_1_acc_29: 0.4500 - dense_1_acc_30: 0.0000e+00     
Epoch 17/100
60/60 [==============================] - 0s - loss: 66.2304 - dense_1_loss_1: 4.1488 - dense_1_loss_2: 3.6911 - dense_1_loss_3: 3.1042 - dense_1_loss_4: 2.9300 - dense_1_loss_5: 2.5999 - dense_1_loss_6: 2.4128 - dense_1_loss_7: 2.4306 - dense_1_loss_8: 2.2906 - dense_1_loss_9: 2.2434 - dense_1_loss_10: 2.1170 - dense_1_loss_11: 2.0371 - dense_1_loss_12: 2.1215 - dense_1_loss_13: 1.9140 - dense_1_loss_14: 1.9231 - dense_1_loss_15: 2.0423 - dense_1_loss_16: 2.0871 - dense_1_loss_17: 2.0013 - dense_1_loss_18: 2.0237 - dense_1_loss_19: 2.0139 - dense_1_loss_20: 2.1063 - dense_1_loss_21: 1.9310 - dense_1_loss_22: 1.9749 - dense_1_loss_23: 1.9782 - dense_1_loss_24: 2.0688 - dense_1_loss_25: 2.1276 - dense_1_loss_26: 1.8247 - dense_1_loss_27: 2.1159 - dense_1_loss_28: 1.9633 - dense_1_loss_29: 2.0072 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.3000 - dense_1_acc_7: 0.3667 - dense_1_acc_8: 0.3667 - dense_1_acc_9: 0.4167 - dense_1_acc_10: 0.3667 - dense_1_acc_11: 0.4667 - dense_1_acc_12: 0.3333 - dense_1_acc_13: 0.4333 - dense_1_acc_14: 0.3667 - dense_1_acc_15: 0.4000 - dense_1_acc_16: 0.3500 - dense_1_acc_17: 0.3333 - dense_1_acc_18: 0.3833 - dense_1_acc_19: 0.4167 - dense_1_acc_20: 0.3333 - dense_1_acc_21: 0.4333 - dense_1_acc_22: 0.4333 - dense_1_acc_23: 0.4167 - dense_1_acc_24: 0.4167 - dense_1_acc_25: 0.2500 - dense_1_acc_26: 0.5167 - dense_1_acc_27: 0.3833 - dense_1_acc_28: 0.4833 - dense_1_acc_29: 0.4167 - dense_1_acc_30: 0.0000e+00     
Epoch 18/100
60/60 [==============================] - 0s - loss: 62.3319 - dense_1_loss_1: 4.1397 - dense_1_loss_2: 3.6421 - dense_1_loss_3: 3.0151 - dense_1_loss_4: 2.8130 - dense_1_loss_5: 2.4758 - dense_1_loss_6: 2.2840 - dense_1_loss_7: 2.2531 - dense_1_loss_8: 2.1385 - dense_1_loss_9: 2.0560 - dense_1_loss_10: 1.9909 - dense_1_loss_11: 1.8967 - dense_1_loss_12: 1.9279 - dense_1_loss_13: 1.7948 - dense_1_loss_14: 1.8012 - dense_1_loss_15: 1.8786 - dense_1_loss_16: 1.9568 - dense_1_loss_17: 1.8149 - dense_1_loss_18: 1.8518 - dense_1_loss_19: 1.8860 - dense_1_loss_20: 1.8862 - dense_1_loss_21: 1.8118 - dense_1_loss_22: 1.8525 - dense_1_loss_23: 1.8370 - dense_1_loss_24: 1.9205 - dense_1_loss_25: 2.0064 - dense_1_loss_26: 1.7062 - dense_1_loss_27: 1.9795 - dense_1_loss_28: 1.8381 - dense_1_loss_29: 1.8766 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.3167 - dense_1_acc_7: 0.3833 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.3833 - dense_1_acc_10: 0.3667 - dense_1_acc_11: 0.4500 - dense_1_acc_12: 0.4167 - dense_1_acc_13: 0.4500 - dense_1_acc_14: 0.4833 - dense_1_acc_15: 0.4500 - dense_1_acc_16: 0.4333 - dense_1_acc_17: 0.4333 - dense_1_acc_18: 0.4333 - dense_1_acc_19: 0.4000 - dense_1_acc_20: 0.4667 - dense_1_acc_21: 0.4000 - dense_1_acc_22: 0.5000 - dense_1_acc_23: 0.4667 - dense_1_acc_24: 0.4500 - dense_1_acc_25: 0.3833 - dense_1_acc_26: 0.6000 - dense_1_acc_27: 0.4167 - dense_1_acc_28: 0.4833 - dense_1_acc_29: 0.4833 - dense_1_acc_30: 0.0000e+00     
Epoch 19/100
60/60 [==============================] - 0s - loss: 58.7765 - dense_1_loss_1: 4.1293 - dense_1_loss_2: 3.5938 - dense_1_loss_3: 2.9217 - dense_1_loss_4: 2.6930 - dense_1_loss_5: 2.3465 - dense_1_loss_6: 2.1640 - dense_1_loss_7: 2.1080 - dense_1_loss_8: 1.9949 - dense_1_loss_9: 1.9220 - dense_1_loss_10: 1.8766 - dense_1_loss_11: 1.7984 - dense_1_loss_12: 1.7816 - dense_1_loss_13: 1.6254 - dense_1_loss_14: 1.6782 - dense_1_loss_15: 1.7419 - dense_1_loss_16: 1.8087 - dense_1_loss_17: 1.6636 - dense_1_loss_18: 1.7438 - dense_1_loss_19: 1.7404 - dense_1_loss_20: 1.7639 - dense_1_loss_21: 1.6986 - dense_1_loss_22: 1.7596 - dense_1_loss_23: 1.6799 - dense_1_loss_24: 1.8050 - dense_1_loss_25: 1.8290 - dense_1_loss_26: 1.5970 - dense_1_loss_27: 1.8552 - dense_1_loss_28: 1.6903 - dense_1_loss_29: 1.7664 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.3333 - dense_1_acc_7: 0.4333 - dense_1_acc_8: 0.4000 - dense_1_acc_9: 0.4167 - dense_1_acc_10: 0.3833 - dense_1_acc_11: 0.5667 - dense_1_acc_12: 0.3833 - dense_1_acc_13: 0.5000 - dense_1_acc_14: 0.5167 - dense_1_acc_15: 0.4500 - dense_1_acc_16: 0.5500 - dense_1_acc_17: 0.5667 - dense_1_acc_18: 0.5167 - dense_1_acc_19: 0.5333 - dense_1_acc_20: 0.5167 - dense_1_acc_21: 0.4500 - dense_1_acc_22: 0.4500 - dense_1_acc_23: 0.5833 - dense_1_acc_24: 0.4833 - dense_1_acc_25: 0.4167 - dense_1_acc_26: 0.6000 - dense_1_acc_27: 0.4667 - dense_1_acc_28: 0.5500 - dense_1_acc_29: 0.4833 - dense_1_acc_30: 0.0000e+00     
Epoch 20/100
60/60 [==============================] - 0s - loss: 55.4639 - dense_1_loss_1: 4.1196 - dense_1_loss_2: 3.5414 - dense_1_loss_3: 2.8298 - dense_1_loss_4: 2.5794 - dense_1_loss_5: 2.2221 - dense_1_loss_6: 2.0297 - dense_1_loss_7: 1.9543 - dense_1_loss_8: 1.8577 - dense_1_loss_9: 1.8207 - dense_1_loss_10: 1.8011 - dense_1_loss_11: 1.6898 - dense_1_loss_12: 1.6834 - dense_1_loss_13: 1.5188 - dense_1_loss_14: 1.5221 - dense_1_loss_15: 1.6620 - dense_1_loss_16: 1.6231 - dense_1_loss_17: 1.5220 - dense_1_loss_18: 1.6622 - dense_1_loss_19: 1.6191 - dense_1_loss_20: 1.5955 - dense_1_loss_21: 1.5492 - dense_1_loss_22: 1.6455 - dense_1_loss_23: 1.6137 - dense_1_loss_24: 1.6356 - dense_1_loss_25: 1.6506 - dense_1_loss_26: 1.5272 - dense_1_loss_27: 1.7468 - dense_1_loss_28: 1.6460 - dense_1_loss_29: 1.5955 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.3667 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.4167 - dense_1_acc_6: 0.4167 - dense_1_acc_7: 0.4833 - dense_1_acc_8: 0.4167 - dense_1_acc_9: 0.4667 - dense_1_acc_10: 0.4000 - dense_1_acc_11: 0.6000 - dense_1_acc_12: 0.5167 - dense_1_acc_13: 0.6833 - dense_1_acc_14: 0.6500 - dense_1_acc_15: 0.5000 - dense_1_acc_16: 0.5500 - dense_1_acc_17: 0.7333 - dense_1_acc_18: 0.4667 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.6167 - dense_1_acc_21: 0.5833 - dense_1_acc_22: 0.5000 - dense_1_acc_23: 0.6333 - dense_1_acc_24: 0.4833 - dense_1_acc_25: 0.4500 - dense_1_acc_26: 0.6333 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.5167 - dense_1_acc_29: 0.6000 - dense_1_acc_30: 0.0000e+00     
Epoch 21/100
60/60 [==============================] - 0s - loss: 52.1706 - dense_1_loss_1: 4.1087 - dense_1_loss_2: 3.4868 - dense_1_loss_3: 2.7327 - dense_1_loss_4: 2.4694 - dense_1_loss_5: 2.1064 - dense_1_loss_6: 1.9156 - dense_1_loss_7: 1.8112 - dense_1_loss_8: 1.7305 - dense_1_loss_9: 1.6741 - dense_1_loss_10: 1.7167 - dense_1_loss_11: 1.6203 - dense_1_loss_12: 1.5757 - dense_1_loss_13: 1.3877 - dense_1_loss_14: 1.4051 - dense_1_loss_15: 1.5279 - dense_1_loss_16: 1.5443 - dense_1_loss_17: 1.3941 - dense_1_loss_18: 1.5216 - dense_1_loss_19: 1.4757 - dense_1_loss_20: 1.4830 - dense_1_loss_21: 1.4610 - dense_1_loss_22: 1.5454 - dense_1_loss_23: 1.4908 - dense_1_loss_24: 1.5218 - dense_1_loss_25: 1.5451 - dense_1_loss_26: 1.4260 - dense_1_loss_27: 1.6116 - dense_1_loss_28: 1.4497 - dense_1_loss_29: 1.4315 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2667 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.4500 - dense_1_acc_6: 0.4833 - dense_1_acc_7: 0.4833 - dense_1_acc_8: 0.4833 - dense_1_acc_9: 0.5667 - dense_1_acc_10: 0.4167 - dense_1_acc_11: 0.6000 - dense_1_acc_12: 0.5000 - dense_1_acc_13: 0.7000 - dense_1_acc_14: 0.6833 - dense_1_acc_15: 0.5333 - dense_1_acc_16: 0.6167 - dense_1_acc_17: 0.7333 - dense_1_acc_18: 0.5500 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.7333 - dense_1_acc_21: 0.6833 - dense_1_acc_22: 0.5333 - dense_1_acc_23: 0.6167 - dense_1_acc_24: 0.6167 - dense_1_acc_25: 0.5500 - dense_1_acc_26: 0.7167 - dense_1_acc_27: 0.5500 - dense_1_acc_28: 0.7000 - dense_1_acc_29: 0.7000 - dense_1_acc_30: 0.0000e+00     
Epoch 22/100
60/60 [==============================] - 0s - loss: 48.9710 - dense_1_loss_1: 4.1000 - dense_1_loss_2: 3.4322 - dense_1_loss_3: 2.6381 - dense_1_loss_4: 2.3589 - dense_1_loss_5: 1.9885 - dense_1_loss_6: 1.7809 - dense_1_loss_7: 1.6797 - dense_1_loss_8: 1.5749 - dense_1_loss_9: 1.6043 - dense_1_loss_10: 1.5899 - dense_1_loss_11: 1.5283 - dense_1_loss_12: 1.4156 - dense_1_loss_13: 1.2840 - dense_1_loss_14: 1.2885 - dense_1_loss_15: 1.3844 - dense_1_loss_16: 1.4373 - dense_1_loss_17: 1.2913 - dense_1_loss_18: 1.4478 - dense_1_loss_19: 1.3420 - dense_1_loss_20: 1.3338 - dense_1_loss_21: 1.3325 - dense_1_loss_22: 1.4579 - dense_1_loss_23: 1.3715 - dense_1_loss_24: 1.4081 - dense_1_loss_25: 1.4006 - dense_1_loss_26: 1.3544 - dense_1_loss_27: 1.5022 - dense_1_loss_28: 1.3299 - dense_1_loss_29: 1.3135 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.4333 - dense_1_acc_6: 0.4833 - dense_1_acc_7: 0.5833 - dense_1_acc_8: 0.5667 - dense_1_acc_9: 0.5667 - dense_1_acc_10: 0.4833 - dense_1_acc_11: 0.6500 - dense_1_acc_12: 0.5833 - dense_1_acc_13: 0.6833 - dense_1_acc_14: 0.7000 - dense_1_acc_15: 0.6333 - dense_1_acc_16: 0.6167 - dense_1_acc_17: 0.7667 - dense_1_acc_18: 0.6167 - dense_1_acc_19: 0.7833 - dense_1_acc_20: 0.7500 - dense_1_acc_21: 0.7500 - dense_1_acc_22: 0.5500 - dense_1_acc_23: 0.7167 - dense_1_acc_24: 0.6333 - dense_1_acc_25: 0.6500 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.6500 - dense_1_acc_28: 0.7500 - dense_1_acc_29: 0.7000 - dense_1_acc_30: 0.0000e+00     
Epoch 23/100
60/60 [==============================] - 0s - loss: 46.0550 - dense_1_loss_1: 4.0902 - dense_1_loss_2: 3.3763 - dense_1_loss_3: 2.5458 - dense_1_loss_4: 2.2399 - dense_1_loss_5: 1.8774 - dense_1_loss_6: 1.6535 - dense_1_loss_7: 1.5304 - dense_1_loss_8: 1.4578 - dense_1_loss_9: 1.4931 - dense_1_loss_10: 1.4437 - dense_1_loss_11: 1.4144 - dense_1_loss_12: 1.2828 - dense_1_loss_13: 1.2014 - dense_1_loss_14: 1.1796 - dense_1_loss_15: 1.2369 - dense_1_loss_16: 1.3292 - dense_1_loss_17: 1.2031 - dense_1_loss_18: 1.3290 - dense_1_loss_19: 1.2642 - dense_1_loss_20: 1.2488 - dense_1_loss_21: 1.2321 - dense_1_loss_22: 1.3522 - dense_1_loss_23: 1.2999 - dense_1_loss_24: 1.2890 - dense_1_loss_25: 1.3212 - dense_1_loss_26: 1.2776 - dense_1_loss_27: 1.3955 - dense_1_loss_28: 1.2376 - dense_1_loss_29: 1.2526 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.4333 - dense_1_acc_6: 0.5333 - dense_1_acc_7: 0.6500 - dense_1_acc_8: 0.6167 - dense_1_acc_9: 0.6000 - dense_1_acc_10: 0.5667 - dense_1_acc_11: 0.6500 - dense_1_acc_12: 0.6667 - dense_1_acc_13: 0.8000 - dense_1_acc_14: 0.7333 - dense_1_acc_15: 0.7500 - dense_1_acc_16: 0.6833 - dense_1_acc_17: 0.8500 - dense_1_acc_18: 0.6667 - dense_1_acc_19: 0.7333 - dense_1_acc_20: 0.8333 - dense_1_acc_21: 0.8000 - dense_1_acc_22: 0.6667 - dense_1_acc_23: 0.7500 - dense_1_acc_24: 0.6667 - dense_1_acc_25: 0.7000 - dense_1_acc_26: 0.7167 - dense_1_acc_27: 0.6500 - dense_1_acc_28: 0.8667 - dense_1_acc_29: 0.7500 - dense_1_acc_30: 0.0000e+00     
Epoch 24/100
60/60 [==============================] - 0s - loss: 43.4850 - dense_1_loss_1: 4.0810 - dense_1_loss_2: 3.3197 - dense_1_loss_3: 2.4572 - dense_1_loss_4: 2.1246 - dense_1_loss_5: 1.7856 - dense_1_loss_6: 1.5532 - dense_1_loss_7: 1.3991 - dense_1_loss_8: 1.3672 - dense_1_loss_9: 1.3770 - dense_1_loss_10: 1.3611 - dense_1_loss_11: 1.3224 - dense_1_loss_12: 1.2066 - dense_1_loss_13: 1.1076 - dense_1_loss_14: 1.1156 - dense_1_loss_15: 1.1495 - dense_1_loss_16: 1.2411 - dense_1_loss_17: 1.1202 - dense_1_loss_18: 1.2157 - dense_1_loss_19: 1.1881 - dense_1_loss_20: 1.2092 - dense_1_loss_21: 1.1384 - dense_1_loss_22: 1.2132 - dense_1_loss_23: 1.2221 - dense_1_loss_24: 1.2087 - dense_1_loss_25: 1.2098 - dense_1_loss_26: 1.1633 - dense_1_loss_27: 1.2749 - dense_1_loss_28: 1.1503 - dense_1_loss_29: 1.2028 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.4167 - dense_1_acc_6: 0.5667 - dense_1_acc_7: 0.6833 - dense_1_acc_8: 0.6833 - dense_1_acc_9: 0.6167 - dense_1_acc_10: 0.6333 - dense_1_acc_11: 0.6667 - dense_1_acc_12: 0.7500 - dense_1_acc_13: 0.8000 - dense_1_acc_14: 0.7167 - dense_1_acc_15: 0.8000 - dense_1_acc_16: 0.7500 - dense_1_acc_17: 0.8333 - dense_1_acc_18: 0.7167 - dense_1_acc_19: 0.7833 - dense_1_acc_20: 0.8000 - dense_1_acc_21: 0.7833 - dense_1_acc_22: 0.7833 - dense_1_acc_23: 0.7333 - dense_1_acc_24: 0.7167 - dense_1_acc_25: 0.7000 - dense_1_acc_26: 0.7333 - dense_1_acc_27: 0.7000 - dense_1_acc_28: 0.8167 - dense_1_acc_29: 0.7833 - dense_1_acc_30: 0.0000e+00     
Epoch 25/100
60/60 [==============================] - 0s - loss: 40.8146 - dense_1_loss_1: 4.0714 - dense_1_loss_2: 3.2611 - dense_1_loss_3: 2.3723 - dense_1_loss_4: 2.0120 - dense_1_loss_5: 1.6697 - dense_1_loss_6: 1.4262 - dense_1_loss_7: 1.2887 - dense_1_loss_8: 1.2742 - dense_1_loss_9: 1.2645 - dense_1_loss_10: 1.2541 - dense_1_loss_11: 1.2271 - dense_1_loss_12: 1.1437 - dense_1_loss_13: 0.9847 - dense_1_loss_14: 1.0188 - dense_1_loss_15: 1.0460 - dense_1_loss_16: 1.1304 - dense_1_loss_17: 1.0321 - dense_1_loss_18: 1.1291 - dense_1_loss_19: 1.1186 - dense_1_loss_20: 1.0755 - dense_1_loss_21: 1.0576 - dense_1_loss_22: 1.1128 - dense_1_loss_23: 1.1334 - dense_1_loss_24: 1.1490 - dense_1_loss_25: 1.1071 - dense_1_loss_26: 1.1069 - dense_1_loss_27: 1.1911 - dense_1_loss_28: 1.0716 - dense_1_loss_29: 1.0849 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.4667 - dense_1_acc_6: 0.6500 - dense_1_acc_7: 0.7833 - dense_1_acc_8: 0.6833 - dense_1_acc_9: 0.6667 - dense_1_acc_10: 0.6833 - dense_1_acc_11: 0.7000 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.8833 - dense_1_acc_14: 0.8667 - dense_1_acc_15: 0.8333 - dense_1_acc_16: 0.7333 - dense_1_acc_17: 0.9167 - dense_1_acc_18: 0.8167 - dense_1_acc_19: 0.8167 - dense_1_acc_20: 0.8833 - dense_1_acc_21: 0.8167 - dense_1_acc_22: 0.8333 - dense_1_acc_23: 0.7667 - dense_1_acc_24: 0.7000 - dense_1_acc_25: 0.7500 - dense_1_acc_26: 0.7667 - dense_1_acc_27: 0.7167 - dense_1_acc_28: 0.8500 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00         
Epoch 26/100
60/60 [==============================] - 0s - loss: 38.3175 - dense_1_loss_1: 4.0618 - dense_1_loss_2: 3.2017 - dense_1_loss_3: 2.2836 - dense_1_loss_4: 1.9052 - dense_1_loss_5: 1.5765 - dense_1_loss_6: 1.3229 - dense_1_loss_7: 1.1677 - dense_1_loss_8: 1.1717 - dense_1_loss_9: 1.1745 - dense_1_loss_10: 1.1258 - dense_1_loss_11: 1.1417 - dense_1_loss_12: 1.0453 - dense_1_loss_13: 0.8932 - dense_1_loss_14: 0.9421 - dense_1_loss_15: 0.9562 - dense_1_loss_16: 1.0254 - dense_1_loss_17: 0.9611 - dense_1_loss_18: 1.0310 - dense_1_loss_19: 1.0220 - dense_1_loss_20: 0.9930 - dense_1_loss_21: 1.0052 - dense_1_loss_22: 1.0159 - dense_1_loss_23: 1.0510 - dense_1_loss_24: 1.0509 - dense_1_loss_25: 1.0435 - dense_1_loss_26: 1.0095 - dense_1_loss_27: 1.0892 - dense_1_loss_28: 1.0074 - dense_1_loss_29: 1.0422 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.4833 - dense_1_acc_6: 0.6833 - dense_1_acc_7: 0.8000 - dense_1_acc_8: 0.7500 - dense_1_acc_9: 0.8000 - dense_1_acc_10: 0.8000 - dense_1_acc_11: 0.7333 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.8333 - dense_1_acc_15: 0.8667 - dense_1_acc_16: 0.8000 - dense_1_acc_17: 0.9167 - dense_1_acc_18: 0.9000 - dense_1_acc_19: 0.8500 - dense_1_acc_20: 0.9000 - dense_1_acc_21: 0.8500 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.8000 - dense_1_acc_24: 0.7833 - dense_1_acc_25: 0.8333 - dense_1_acc_26: 0.8333 - dense_1_acc_27: 0.8000 - dense_1_acc_28: 0.8667 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00     
Epoch 27/100
60/60 [==============================] - 0s - loss: 36.0205 - dense_1_loss_1: 4.0515 - dense_1_loss_2: 3.1443 - dense_1_loss_3: 2.2020 - dense_1_loss_4: 1.7932 - dense_1_loss_5: 1.4843 - dense_1_loss_6: 1.2328 - dense_1_loss_7: 1.0851 - dense_1_loss_8: 1.0801 - dense_1_loss_9: 1.0869 - dense_1_loss_10: 1.0429 - dense_1_loss_11: 1.0767 - dense_1_loss_12: 0.9757 - dense_1_loss_13: 0.8379 - dense_1_loss_14: 0.8643 - dense_1_loss_15: 0.8900 - dense_1_loss_16: 0.9171 - dense_1_loss_17: 0.9034 - dense_1_loss_18: 0.9265 - dense_1_loss_19: 0.9491 - dense_1_loss_20: 0.9510 - dense_1_loss_21: 0.9288 - dense_1_loss_22: 0.9309 - dense_1_loss_23: 0.9326 - dense_1_loss_24: 0.9740 - dense_1_loss_25: 1.0095 - dense_1_loss_26: 0.9176 - dense_1_loss_27: 0.9656 - dense_1_loss_28: 0.8980 - dense_1_loss_29: 0.9687 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.5667 - dense_1_acc_6: 0.6833 - dense_1_acc_7: 0.8000 - dense_1_acc_8: 0.8000 - dense_1_acc_9: 0.7833 - dense_1_acc_10: 0.8167 - dense_1_acc_11: 0.7500 - dense_1_acc_12: 0.8167 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.8833 - dense_1_acc_15: 0.9000 - dense_1_acc_16: 0.8500 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 0.9167 - dense_1_acc_19: 0.8500 - dense_1_acc_20: 0.9000 - dense_1_acc_21: 0.8500 - dense_1_acc_22: 0.8833 - dense_1_acc_23: 0.9000 - dense_1_acc_24: 0.8500 - dense_1_acc_25: 0.7833 - dense_1_acc_26: 0.8500 - dense_1_acc_27: 0.9000 - dense_1_acc_28: 0.9167 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00     
Epoch 28/100
60/60 [==============================] - 0s - loss: 33.7848 - dense_1_loss_1: 4.0425 - dense_1_loss_2: 3.0868 - dense_1_loss_3: 2.1200 - dense_1_loss_4: 1.6852 - dense_1_loss_5: 1.3932 - dense_1_loss_6: 1.1289 - dense_1_loss_7: 0.9900 - dense_1_loss_8: 1.0024 - dense_1_loss_9: 1.0050 - dense_1_loss_10: 0.9488 - dense_1_loss_11: 0.9817 - dense_1_loss_12: 0.8831 - dense_1_loss_13: 0.7756 - dense_1_loss_14: 0.7834 - dense_1_loss_15: 0.8331 - dense_1_loss_16: 0.8348 - dense_1_loss_17: 0.8140 - dense_1_loss_18: 0.8562 - dense_1_loss_19: 0.8758 - dense_1_loss_20: 0.8798 - dense_1_loss_21: 0.8379 - dense_1_loss_22: 0.8613 - dense_1_loss_23: 0.8792 - dense_1_loss_24: 0.8986 - dense_1_loss_25: 0.9192 - dense_1_loss_26: 0.8616 - dense_1_loss_27: 0.8881 - dense_1_loss_28: 0.8326 - dense_1_loss_29: 0.8860 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.6167 - dense_1_acc_6: 0.7167 - dense_1_acc_7: 0.8500 - dense_1_acc_8: 0.9000 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.9000 - dense_1_acc_11: 0.8000 - dense_1_acc_12: 0.8333 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9333 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9500 - dense_1_acc_18: 0.9500 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 0.9333 - dense_1_acc_23: 0.8833 - dense_1_acc_24: 0.8833 - dense_1_acc_25: 0.8667 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9333 - dense_1_acc_28: 0.9500 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0000e+00     
Epoch 29/100
60/60 [==============================] - 0s - loss: 31.6634 - dense_1_loss_1: 4.0341 - dense_1_loss_2: 3.0323 - dense_1_loss_3: 2.0400 - dense_1_loss_4: 1.5919 - dense_1_loss_5: 1.2979 - dense_1_loss_6: 1.0361 - dense_1_loss_7: 0.9105 - dense_1_loss_8: 0.9188 - dense_1_loss_9: 0.9485 - dense_1_loss_10: 0.8638 - dense_1_loss_11: 0.8978 - dense_1_loss_12: 0.8210 - dense_1_loss_13: 0.7204 - dense_1_loss_14: 0.7183 - dense_1_loss_15: 0.7616 - dense_1_loss_16: 0.7708 - dense_1_loss_17: 0.7434 - dense_1_loss_18: 0.8088 - dense_1_loss_19: 0.7975 - dense_1_loss_20: 0.8116 - dense_1_loss_21: 0.7497 - dense_1_loss_22: 0.7999 - dense_1_loss_23: 0.7813 - dense_1_loss_24: 0.8122 - dense_1_loss_25: 0.8206 - dense_1_loss_26: 0.7877 - dense_1_loss_27: 0.8113 - dense_1_loss_28: 0.7804 - dense_1_loss_29: 0.7951 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.4000 - dense_1_acc_5: 0.6833 - dense_1_acc_6: 0.7500 - dense_1_acc_7: 0.9000 - dense_1_acc_8: 0.9000 - dense_1_acc_9: 0.8833 - dense_1_acc_10: 0.9333 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9000 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9000 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9333 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9167 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.9167 - dense_1_acc_25: 0.9167 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 30/100
60/60 [==============================] - 0s - loss: 29.7061 - dense_1_loss_1: 4.0260 - dense_1_loss_2: 2.9749 - dense_1_loss_3: 1.9639 - dense_1_loss_4: 1.4964 - dense_1_loss_5: 1.2097 - dense_1_loss_6: 0.9430 - dense_1_loss_7: 0.8519 - dense_1_loss_8: 0.8415 - dense_1_loss_9: 0.8529 - dense_1_loss_10: 0.7872 - dense_1_loss_11: 0.8304 - dense_1_loss_12: 0.7575 - dense_1_loss_13: 0.6494 - dense_1_loss_14: 0.6410 - dense_1_loss_15: 0.6851 - dense_1_loss_16: 0.6922 - dense_1_loss_17: 0.6846 - dense_1_loss_18: 0.7506 - dense_1_loss_19: 0.7225 - dense_1_loss_20: 0.7484 - dense_1_loss_21: 0.6899 - dense_1_loss_22: 0.7451 - dense_1_loss_23: 0.7032 - dense_1_loss_24: 0.7487 - dense_1_loss_25: 0.7781 - dense_1_loss_26: 0.7211 - dense_1_loss_27: 0.7470 - dense_1_loss_28: 0.7125 - dense_1_loss_29: 0.7516 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4833 - dense_1_acc_4: 0.4667 - dense_1_acc_5: 0.7000 - dense_1_acc_6: 0.8333 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.9333 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 0.9500 - dense_1_acc_11: 0.8667 - dense_1_acc_12: 0.9333 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9500 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 0.9333 - dense_1_acc_23: 0.9500 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9667 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 31/100
60/60 [==============================] - 0s - loss: 27.8177 - dense_1_loss_1: 4.0181 - dense_1_loss_2: 2.9233 - dense_1_loss_3: 1.8878 - dense_1_loss_4: 1.4001 - dense_1_loss_5: 1.1331 - dense_1_loss_6: 0.8527 - dense_1_loss_7: 0.7803 - dense_1_loss_8: 0.7760 - dense_1_loss_9: 0.7796 - dense_1_loss_10: 0.7254 - dense_1_loss_11: 0.7567 - dense_1_loss_12: 0.6858 - dense_1_loss_13: 0.5967 - dense_1_loss_14: 0.5906 - dense_1_loss_15: 0.6053 - dense_1_loss_16: 0.6292 - dense_1_loss_17: 0.6285 - dense_1_loss_18: 0.6745 - dense_1_loss_19: 0.6555 - dense_1_loss_20: 0.6847 - dense_1_loss_21: 0.6409 - dense_1_loss_22: 0.6845 - dense_1_loss_23: 0.6301 - dense_1_loss_24: 0.6706 - dense_1_loss_25: 0.7265 - dense_1_loss_26: 0.6745 - dense_1_loss_27: 0.6844 - dense_1_loss_28: 0.6329 - dense_1_loss_29: 0.6896 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.4833 - dense_1_acc_4: 0.5333 - dense_1_acc_5: 0.7167 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9167 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9667 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 32/100
60/60 [==============================] - 0s - loss: 26.0561 - dense_1_loss_1: 4.0111 - dense_1_loss_2: 2.8688 - dense_1_loss_3: 1.8167 - dense_1_loss_4: 1.3135 - dense_1_loss_5: 1.0543 - dense_1_loss_6: 0.7921 - dense_1_loss_7: 0.7131 - dense_1_loss_8: 0.7027 - dense_1_loss_9: 0.7253 - dense_1_loss_10: 0.6496 - dense_1_loss_11: 0.6921 - dense_1_loss_12: 0.6136 - dense_1_loss_13: 0.5311 - dense_1_loss_14: 0.5145 - dense_1_loss_15: 0.5617 - dense_1_loss_16: 0.5770 - dense_1_loss_17: 0.5688 - dense_1_loss_18: 0.6178 - dense_1_loss_19: 0.6038 - dense_1_loss_20: 0.6121 - dense_1_loss_21: 0.5781 - dense_1_loss_22: 0.6214 - dense_1_loss_23: 0.5971 - dense_1_loss_24: 0.6164 - dense_1_loss_25: 0.6363 - dense_1_loss_26: 0.6279 - dense_1_loss_27: 0.6188 - dense_1_loss_28: 0.5768 - dense_1_loss_29: 0.6438 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.5167 - dense_1_acc_4: 0.6667 - dense_1_acc_5: 0.7500 - dense_1_acc_6: 0.8667 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9333 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 33/100
60/60 [==============================] - 0s - loss: 24.4278 - dense_1_loss_1: 4.0040 - dense_1_loss_2: 2.8121 - dense_1_loss_3: 1.7462 - dense_1_loss_4: 1.2290 - dense_1_loss_5: 0.9743 - dense_1_loss_6: 0.7325 - dense_1_loss_7: 0.6551 - dense_1_loss_8: 0.6464 - dense_1_loss_9: 0.6683 - dense_1_loss_10: 0.5891 - dense_1_loss_11: 0.6487 - dense_1_loss_12: 0.5629 - dense_1_loss_13: 0.4798 - dense_1_loss_14: 0.4778 - dense_1_loss_15: 0.5141 - dense_1_loss_16: 0.5406 - dense_1_loss_17: 0.5093 - dense_1_loss_18: 0.5536 - dense_1_loss_19: 0.5704 - dense_1_loss_20: 0.5526 - dense_1_loss_21: 0.5321 - dense_1_loss_22: 0.5522 - dense_1_loss_23: 0.5227 - dense_1_loss_24: 0.5615 - dense_1_loss_25: 0.5688 - dense_1_loss_26: 0.5624 - dense_1_loss_27: 0.5525 - dense_1_loss_28: 0.5284 - dense_1_loss_29: 0.5805 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.7000 - dense_1_acc_5: 0.8333 - dense_1_acc_6: 0.9000 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 0.9667 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 34/100
60/60 [==============================] - 0s - loss: 22.9095 - dense_1_loss_1: 3.9968 - dense_1_loss_2: 2.7601 - dense_1_loss_3: 1.6792 - dense_1_loss_4: 1.1448 - dense_1_loss_5: 0.8968 - dense_1_loss_6: 0.6668 - dense_1_loss_7: 0.6069 - dense_1_loss_8: 0.5920 - dense_1_loss_9: 0.6197 - dense_1_loss_10: 0.5248 - dense_1_loss_11: 0.5940 - dense_1_loss_12: 0.5129 - dense_1_loss_13: 0.4285 - dense_1_loss_14: 0.4392 - dense_1_loss_15: 0.4631 - dense_1_loss_16: 0.5002 - dense_1_loss_17: 0.4625 - dense_1_loss_18: 0.5015 - dense_1_loss_19: 0.5122 - dense_1_loss_20: 0.5092 - dense_1_loss_21: 0.4810 - dense_1_loss_22: 0.4989 - dense_1_loss_23: 0.4674 - dense_1_loss_24: 0.5078 - dense_1_loss_25: 0.5154 - dense_1_loss_26: 0.5083 - dense_1_loss_27: 0.5024 - dense_1_loss_28: 0.4902 - dense_1_loss_29: 0.5269 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5833 - dense_1_acc_4: 0.7000 - dense_1_acc_5: 0.8500 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9500 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 35/100
60/60 [==============================] - 0s - loss: 21.5532 - dense_1_loss_1: 3.9900 - dense_1_loss_2: 2.7059 - dense_1_loss_3: 1.6127 - dense_1_loss_4: 1.0590 - dense_1_loss_5: 0.8290 - dense_1_loss_6: 0.6149 - dense_1_loss_7: 0.5573 - dense_1_loss_8: 0.5397 - dense_1_loss_9: 0.5651 - dense_1_loss_10: 0.4755 - dense_1_loss_11: 0.5361 - dense_1_loss_12: 0.4597 - dense_1_loss_13: 0.3866 - dense_1_loss_14: 0.3862 - dense_1_loss_15: 0.4262 - dense_1_loss_16: 0.4494 - dense_1_loss_17: 0.4241 - dense_1_loss_18: 0.4588 - dense_1_loss_19: 0.4541 - dense_1_loss_20: 0.4694 - dense_1_loss_21: 0.4399 - dense_1_loss_22: 0.4486 - dense_1_loss_23: 0.4392 - dense_1_loss_24: 0.4614 - dense_1_loss_25: 0.4768 - dense_1_loss_26: 0.4770 - dense_1_loss_27: 0.4658 - dense_1_loss_28: 0.4473 - dense_1_loss_29: 0.4975 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.5833 - dense_1_acc_4: 0.7333 - dense_1_acc_5: 0.8667 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9833 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 36/100
60/60 [==============================] - 0s - loss: 20.2400 - dense_1_loss_1: 3.9838 - dense_1_loss_2: 2.6528 - dense_1_loss_3: 1.5476 - dense_1_loss_4: 0.9821 - dense_1_loss_5: 0.7625 - dense_1_loss_6: 0.5707 - dense_1_loss_7: 0.5068 - dense_1_loss_8: 0.4933 - dense_1_loss_9: 0.5105 - dense_1_loss_10: 0.4297 - dense_1_loss_11: 0.4830 - dense_1_loss_12: 0.4188 - dense_1_loss_13: 0.3629 - dense_1_loss_14: 0.3593 - dense_1_loss_15: 0.3770 - dense_1_loss_16: 0.4173 - dense_1_loss_17: 0.3862 - dense_1_loss_18: 0.4134 - dense_1_loss_19: 0.4128 - dense_1_loss_20: 0.4220 - dense_1_loss_21: 0.4019 - dense_1_loss_22: 0.4135 - dense_1_loss_23: 0.3921 - dense_1_loss_24: 0.4180 - dense_1_loss_25: 0.4395 - dense_1_loss_26: 0.4253 - dense_1_loss_27: 0.4206 - dense_1_loss_28: 0.3856 - dense_1_loss_29: 0.4510 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.6000 - dense_1_acc_4: 0.8333 - dense_1_acc_5: 0.8667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9667 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 37/100
60/60 [==============================] - 0s - loss: 19.0656 - dense_1_loss_1: 3.9772 - dense_1_loss_2: 2.6016 - dense_1_loss_3: 1.4883 - dense_1_loss_4: 0.9144 - dense_1_loss_5: 0.7015 - dense_1_loss_6: 0.5254 - dense_1_loss_7: 0.4673 - dense_1_loss_8: 0.4499 - dense_1_loss_9: 0.4649 - dense_1_loss_10: 0.3884 - dense_1_loss_11: 0.4509 - dense_1_loss_12: 0.3795 - dense_1_loss_13: 0.3233 - dense_1_loss_14: 0.3257 - dense_1_loss_15: 0.3449 - dense_1_loss_16: 0.3730 - dense_1_loss_17: 0.3505 - dense_1_loss_18: 0.3741 - dense_1_loss_19: 0.3721 - dense_1_loss_20: 0.3834 - dense_1_loss_21: 0.3614 - dense_1_loss_22: 0.3741 - dense_1_loss_23: 0.3577 - dense_1_loss_24: 0.3770 - dense_1_loss_25: 0.3978 - dense_1_loss_26: 0.3797 - dense_1_loss_27: 0.3793 - dense_1_loss_28: 0.3593 - dense_1_loss_29: 0.4232 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.5833 - dense_1_acc_4: 0.8333 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 38/100
60/60 [==============================] - 0s - loss: 17.9676 - dense_1_loss_1: 3.9711 - dense_1_loss_2: 2.5485 - dense_1_loss_3: 1.4286 - dense_1_loss_4: 0.8440 - dense_1_loss_5: 0.6445 - dense_1_loss_6: 0.4791 - dense_1_loss_7: 0.4292 - dense_1_loss_8: 0.4123 - dense_1_loss_9: 0.4177 - dense_1_loss_10: 0.3490 - dense_1_loss_11: 0.4141 - dense_1_loss_12: 0.3406 - dense_1_loss_13: 0.2968 - dense_1_loss_14: 0.3029 - dense_1_loss_15: 0.3080 - dense_1_loss_16: 0.3358 - dense_1_loss_17: 0.3232 - dense_1_loss_18: 0.3373 - dense_1_loss_19: 0.3404 - dense_1_loss_20: 0.3490 - dense_1_loss_21: 0.3327 - dense_1_loss_22: 0.3346 - dense_1_loss_23: 0.3261 - dense_1_loss_24: 0.3371 - dense_1_loss_25: 0.3630 - dense_1_loss_26: 0.3500 - dense_1_loss_27: 0.3456 - dense_1_loss_28: 0.3307 - dense_1_loss_29: 0.3756 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.8333 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 0.9833 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 39/100
60/60 [==============================] - 0s - loss: 16.9770 - dense_1_loss_1: 3.9655 - dense_1_loss_2: 2.4968 - dense_1_loss_3: 1.3715 - dense_1_loss_4: 0.7846 - dense_1_loss_5: 0.5948 - dense_1_loss_6: 0.4417 - dense_1_loss_7: 0.3954 - dense_1_loss_8: 0.3779 - dense_1_loss_9: 0.3790 - dense_1_loss_10: 0.3163 - dense_1_loss_11: 0.3756 - dense_1_loss_12: 0.3138 - dense_1_loss_13: 0.2644 - dense_1_loss_14: 0.2830 - dense_1_loss_15: 0.2780 - dense_1_loss_16: 0.3080 - dense_1_loss_17: 0.2951 - dense_1_loss_18: 0.3061 - dense_1_loss_19: 0.3097 - dense_1_loss_20: 0.3185 - dense_1_loss_21: 0.2992 - dense_1_loss_22: 0.3099 - dense_1_loss_23: 0.2835 - dense_1_loss_24: 0.3036 - dense_1_loss_25: 0.3281 - dense_1_loss_26: 0.3320 - dense_1_loss_27: 0.3111 - dense_1_loss_28: 0.2954 - dense_1_loss_29: 0.3384 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9167 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 40/100
60/60 [==============================] - 0s - loss: 16.0670 - dense_1_loss_1: 3.9596 - dense_1_loss_2: 2.4460 - dense_1_loss_3: 1.3226 - dense_1_loss_4: 0.7254 - dense_1_loss_5: 0.5457 - dense_1_loss_6: 0.4076 - dense_1_loss_7: 0.3611 - dense_1_loss_8: 0.3347 - dense_1_loss_9: 0.3472 - dense_1_loss_10: 0.2849 - dense_1_loss_11: 0.3420 - dense_1_loss_12: 0.2801 - dense_1_loss_13: 0.2389 - dense_1_loss_14: 0.2436 - dense_1_loss_15: 0.2635 - dense_1_loss_16: 0.2754 - dense_1_loss_17: 0.2704 - dense_1_loss_18: 0.2768 - dense_1_loss_19: 0.2849 - dense_1_loss_20: 0.2884 - dense_1_loss_21: 0.2716 - dense_1_loss_22: 0.2793 - dense_1_loss_23: 0.2646 - dense_1_loss_24: 0.2760 - dense_1_loss_25: 0.2976 - dense_1_loss_26: 0.3049 - dense_1_loss_27: 0.2849 - dense_1_loss_28: 0.2748 - dense_1_loss_29: 0.3146 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9333 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 41/100
60/60 [==============================] - 0s - loss: 15.2529 - dense_1_loss_1: 3.9544 - dense_1_loss_2: 2.3966 - dense_1_loss_3: 1.2687 - dense_1_loss_4: 0.6774 - dense_1_loss_5: 0.5024 - dense_1_loss_6: 0.3812 - dense_1_loss_7: 0.3285 - dense_1_loss_8: 0.3046 - dense_1_loss_9: 0.3156 - dense_1_loss_10: 0.2589 - dense_1_loss_11: 0.3035 - dense_1_loss_12: 0.2607 - dense_1_loss_13: 0.2246 - dense_1_loss_14: 0.2462 - dense_1_loss_15: 0.2265 - dense_1_loss_16: 0.2568 - dense_1_loss_17: 0.2383 - dense_1_loss_18: 0.2535 - dense_1_loss_19: 0.2576 - dense_1_loss_20: 0.2611 - dense_1_loss_21: 0.2514 - dense_1_loss_22: 0.2588 - dense_1_loss_23: 0.2354 - dense_1_loss_24: 0.2524 - dense_1_loss_25: 0.2707 - dense_1_loss_26: 0.2689 - dense_1_loss_27: 0.2652 - dense_1_loss_28: 0.2467 - dense_1_loss_29: 0.2863 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 42/100
60/60 [==============================] - 0s - loss: 14.5065 - dense_1_loss_1: 3.9485 - dense_1_loss_2: 2.3516 - dense_1_loss_3: 1.2202 - dense_1_loss_4: 0.6335 - dense_1_loss_5: 0.4658 - dense_1_loss_6: 0.3517 - dense_1_loss_7: 0.3073 - dense_1_loss_8: 0.2741 - dense_1_loss_9: 0.2929 - dense_1_loss_10: 0.2326 - dense_1_loss_11: 0.2891 - dense_1_loss_12: 0.2363 - dense_1_loss_13: 0.2024 - dense_1_loss_14: 0.2065 - dense_1_loss_15: 0.2181 - dense_1_loss_16: 0.2337 - dense_1_loss_17: 0.2180 - dense_1_loss_18: 0.2269 - dense_1_loss_19: 0.2313 - dense_1_loss_20: 0.2425 - dense_1_loss_21: 0.2306 - dense_1_loss_22: 0.2312 - dense_1_loss_23: 0.2107 - dense_1_loss_24: 0.2246 - dense_1_loss_25: 0.2515 - dense_1_loss_26: 0.2455 - dense_1_loss_27: 0.2361 - dense_1_loss_28: 0.2248 - dense_1_loss_29: 0.2686 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 43/100
60/60 [==============================] - 0s - loss: 13.8373 - dense_1_loss_1: 3.9440 - dense_1_loss_2: 2.3025 - dense_1_loss_3: 1.1717 - dense_1_loss_4: 0.5890 - dense_1_loss_5: 0.4301 - dense_1_loss_6: 0.3248 - dense_1_loss_7: 0.2817 - dense_1_loss_8: 0.2546 - dense_1_loss_9: 0.2660 - dense_1_loss_10: 0.2147 - dense_1_loss_11: 0.2686 - dense_1_loss_12: 0.2150 - dense_1_loss_13: 0.1853 - dense_1_loss_14: 0.1848 - dense_1_loss_15: 0.2041 - dense_1_loss_16: 0.2100 - dense_1_loss_17: 0.2053 - dense_1_loss_18: 0.2071 - dense_1_loss_19: 0.2139 - dense_1_loss_20: 0.2197 - dense_1_loss_21: 0.2124 - dense_1_loss_22: 0.2034 - dense_1_loss_23: 0.2008 - dense_1_loss_24: 0.2072 - dense_1_loss_25: 0.2268 - dense_1_loss_26: 0.2216 - dense_1_loss_27: 0.2167 - dense_1_loss_28: 0.2117 - dense_1_loss_29: 0.2436 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 44/100
60/60 [==============================] - 0s - loss: 13.2177 - dense_1_loss_1: 3.9386 - dense_1_loss_2: 2.2562 - dense_1_loss_3: 1.1255 - dense_1_loss_4: 0.5486 - dense_1_loss_5: 0.3993 - dense_1_loss_6: 0.3025 - dense_1_loss_7: 0.2564 - dense_1_loss_8: 0.2345 - dense_1_loss_9: 0.2460 - dense_1_loss_10: 0.1942 - dense_1_loss_11: 0.2437 - dense_1_loss_12: 0.1987 - dense_1_loss_13: 0.1686 - dense_1_loss_14: 0.1807 - dense_1_loss_15: 0.1752 - dense_1_loss_16: 0.1953 - dense_1_loss_17: 0.1844 - dense_1_loss_18: 0.1901 - dense_1_loss_19: 0.1980 - dense_1_loss_20: 0.2013 - dense_1_loss_21: 0.1911 - dense_1_loss_22: 0.1918 - dense_1_loss_23: 0.1833 - dense_1_loss_24: 0.1919 - dense_1_loss_25: 0.1997 - dense_1_loss_26: 0.2072 - dense_1_loss_27: 0.2015 - dense_1_loss_28: 0.1940 - dense_1_loss_29: 0.2194 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9667 - dense_1_acc_30: 0.0000e+00     
Epoch 45/100
60/60 [==============================] - 0s - loss: 12.6360 - dense_1_loss_1: 3.9345 - dense_1_loss_2: 2.2119 - dense_1_loss_3: 1.0799 - dense_1_loss_4: 0.5121 - dense_1_loss_5: 0.3681 - dense_1_loss_6: 0.2783 - dense_1_loss_7: 0.2343 - dense_1_loss_8: 0.2134 - dense_1_loss_9: 0.2234 - dense_1_loss_10: 0.1771 - dense_1_loss_11: 0.2244 - dense_1_loss_12: 0.1790 - dense_1_loss_13: 0.1555 - dense_1_loss_14: 0.1651 - dense_1_loss_15: 0.1633 - dense_1_loss_16: 0.1810 - dense_1_loss_17: 0.1663 - dense_1_loss_18: 0.1743 - dense_1_loss_19: 0.1806 - dense_1_loss_20: 0.1841 - dense_1_loss_21: 0.1741 - dense_1_loss_22: 0.1761 - dense_1_loss_23: 0.1632 - dense_1_loss_24: 0.1746 - dense_1_loss_25: 0.1848 - dense_1_loss_26: 0.1918 - dense_1_loss_27: 0.1815 - dense_1_loss_28: 0.1785 - dense_1_loss_29: 0.2050 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9000 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 46/100
60/60 [==============================] - 0s - loss: 12.1317 - dense_1_loss_1: 3.9293 - dense_1_loss_2: 2.1679 - dense_1_loss_3: 1.0403 - dense_1_loss_4: 0.4788 - dense_1_loss_5: 0.3430 - dense_1_loss_6: 0.2594 - dense_1_loss_7: 0.2174 - dense_1_loss_8: 0.1956 - dense_1_loss_9: 0.2044 - dense_1_loss_10: 0.1634 - dense_1_loss_11: 0.2073 - dense_1_loss_12: 0.1628 - dense_1_loss_13: 0.1453 - dense_1_loss_14: 0.1458 - dense_1_loss_15: 0.1542 - dense_1_loss_16: 0.1636 - dense_1_loss_17: 0.1542 - dense_1_loss_18: 0.1606 - dense_1_loss_19: 0.1675 - dense_1_loss_20: 0.1694 - dense_1_loss_21: 0.1628 - dense_1_loss_22: 0.1615 - dense_1_loss_23: 0.1489 - dense_1_loss_24: 0.1579 - dense_1_loss_25: 0.1721 - dense_1_loss_26: 0.1748 - dense_1_loss_27: 0.1673 - dense_1_loss_28: 0.1650 - dense_1_loss_29: 0.1908 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 47/100
60/60 [==============================] - 0s - loss: 11.6713 - dense_1_loss_1: 3.9248 - dense_1_loss_2: 2.1258 - dense_1_loss_3: 1.0013 - dense_1_loss_4: 0.4499 - dense_1_loss_5: 0.3187 - dense_1_loss_6: 0.2443 - dense_1_loss_7: 0.2008 - dense_1_loss_8: 0.1794 - dense_1_loss_9: 0.1882 - dense_1_loss_10: 0.1506 - dense_1_loss_11: 0.1903 - dense_1_loss_12: 0.1520 - dense_1_loss_13: 0.1332 - dense_1_loss_14: 0.1355 - dense_1_loss_15: 0.1385 - dense_1_loss_16: 0.1498 - dense_1_loss_17: 0.1434 - dense_1_loss_18: 0.1492 - dense_1_loss_19: 0.1531 - dense_1_loss_20: 0.1568 - dense_1_loss_21: 0.1519 - dense_1_loss_22: 0.1467 - dense_1_loss_23: 0.1383 - dense_1_loss_24: 0.1460 - dense_1_loss_25: 0.1583 - dense_1_loss_26: 0.1615 - dense_1_loss_27: 0.1581 - dense_1_loss_28: 0.1521 - dense_1_loss_29: 0.1727 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 48/100
60/60 [==============================] - 0s - loss: 11.2458 - dense_1_loss_1: 3.9202 - dense_1_loss_2: 2.0852 - dense_1_loss_3: 0.9666 - dense_1_loss_4: 0.4240 - dense_1_loss_5: 0.2963 - dense_1_loss_6: 0.2271 - dense_1_loss_7: 0.1857 - dense_1_loss_8: 0.1639 - dense_1_loss_9: 0.1754 - dense_1_loss_10: 0.1371 - dense_1_loss_11: 0.1764 - dense_1_loss_12: 0.1424 - dense_1_loss_13: 0.1209 - dense_1_loss_14: 0.1275 - dense_1_loss_15: 0.1261 - dense_1_loss_16: 0.1425 - dense_1_loss_17: 0.1292 - dense_1_loss_18: 0.1379 - dense_1_loss_19: 0.1374 - dense_1_loss_20: 0.1471 - dense_1_loss_21: 0.1382 - dense_1_loss_22: 0.1372 - dense_1_loss_23: 0.1262 - dense_1_loss_24: 0.1348 - dense_1_loss_25: 0.1449 - dense_1_loss_26: 0.1467 - dense_1_loss_27: 0.1492 - dense_1_loss_28: 0.1402 - dense_1_loss_29: 0.1595 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 49/100
60/60 [==============================] - 0s - loss: 10.8676 - dense_1_loss_1: 3.9159 - dense_1_loss_2: 2.0455 - dense_1_loss_3: 0.9305 - dense_1_loss_4: 0.3992 - dense_1_loss_5: 0.2781 - dense_1_loss_6: 0.2127 - dense_1_loss_7: 0.1740 - dense_1_loss_8: 0.1519 - dense_1_loss_9: 0.1637 - dense_1_loss_10: 0.1273 - dense_1_loss_11: 0.1649 - dense_1_loss_12: 0.1308 - dense_1_loss_13: 0.1127 - dense_1_loss_14: 0.1173 - dense_1_loss_15: 0.1182 - dense_1_loss_16: 0.1314 - dense_1_loss_17: 0.1199 - dense_1_loss_18: 0.1290 - dense_1_loss_19: 0.1273 - dense_1_loss_20: 0.1350 - dense_1_loss_21: 0.1272 - dense_1_loss_22: 0.1275 - dense_1_loss_23: 0.1163 - dense_1_loss_24: 0.1237 - dense_1_loss_25: 0.1328 - dense_1_loss_26: 0.1371 - dense_1_loss_27: 0.1371 - dense_1_loss_28: 0.1313 - dense_1_loss_29: 0.1493 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 0.9833 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 50/100
60/60 [==============================] - 0s - loss: 10.5149 - dense_1_loss_1: 3.9115 - dense_1_loss_2: 2.0077 - dense_1_loss_3: 0.8961 - dense_1_loss_4: 0.3733 - dense_1_loss_5: 0.2618 - dense_1_loss_6: 0.1972 - dense_1_loss_7: 0.1602 - dense_1_loss_8: 0.1434 - dense_1_loss_9: 0.1501 - dense_1_loss_10: 0.1187 - dense_1_loss_11: 0.1495 - dense_1_loss_12: 0.1195 - dense_1_loss_13: 0.1068 - dense_1_loss_14: 0.1078 - dense_1_loss_15: 0.1116 - dense_1_loss_16: 0.1175 - dense_1_loss_17: 0.1143 - dense_1_loss_18: 0.1200 - dense_1_loss_19: 0.1194 - dense_1_loss_20: 0.1235 - dense_1_loss_21: 0.1205 - dense_1_loss_22: 0.1171 - dense_1_loss_23: 0.1097 - dense_1_loss_24: 0.1138 - dense_1_loss_25: 0.1241 - dense_1_loss_26: 0.1276 - dense_1_loss_27: 0.1280 - dense_1_loss_28: 0.1236 - dense_1_loss_29: 0.1402 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 0.9833 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 51/100
60/60 [==============================] - 0s - loss: 10.1932 - dense_1_loss_1: 3.9073 - dense_1_loss_2: 1.9695 - dense_1_loss_3: 0.8641 - dense_1_loss_4: 0.3521 - dense_1_loss_5: 0.2467 - dense_1_loss_6: 0.1842 - dense_1_loss_7: 0.1490 - dense_1_loss_8: 0.1352 - dense_1_loss_9: 0.1386 - dense_1_loss_10: 0.1112 - dense_1_loss_11: 0.1381 - dense_1_loss_12: 0.1111 - dense_1_loss_13: 0.1001 - dense_1_loss_14: 0.1017 - dense_1_loss_15: 0.1037 - dense_1_loss_16: 0.1100 - dense_1_loss_17: 0.1060 - dense_1_loss_18: 0.1109 - dense_1_loss_19: 0.1118 - dense_1_loss_20: 0.1147 - dense_1_loss_21: 0.1129 - dense_1_loss_22: 0.1084 - dense_1_loss_23: 0.1017 - dense_1_loss_24: 0.1066 - dense_1_loss_25: 0.1151 - dense_1_loss_26: 0.1180 - dense_1_loss_27: 0.1195 - dense_1_loss_28: 0.1149 - dense_1_loss_29: 0.1299 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.7833 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00    
Epoch 52/100
60/60 [==============================] - 0s - loss: 9.9014 - dense_1_loss_1: 3.9034 - dense_1_loss_2: 1.9349 - dense_1_loss_3: 0.8357 - dense_1_loss_4: 0.3334 - dense_1_loss_5: 0.2317 - dense_1_loss_6: 0.1726 - dense_1_loss_7: 0.1395 - dense_1_loss_8: 0.1267 - dense_1_loss_9: 0.1293 - dense_1_loss_10: 0.1047 - dense_1_loss_11: 0.1293 - dense_1_loss_12: 0.1045 - dense_1_loss_13: 0.0920 - dense_1_loss_14: 0.0955 - dense_1_loss_15: 0.0965 - dense_1_loss_16: 0.1051 - dense_1_loss_17: 0.0976 - dense_1_loss_18: 0.1035 - dense_1_loss_19: 0.1030 - dense_1_loss_20: 0.1081 - dense_1_loss_21: 0.1040 - dense_1_loss_22: 0.1013 - dense_1_loss_23: 0.0944 - dense_1_loss_24: 0.0998 - dense_1_loss_25: 0.1068 - dense_1_loss_26: 0.1091 - dense_1_loss_27: 0.1114 - dense_1_loss_28: 0.1074 - dense_1_loss_29: 0.1202 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 53/100
60/60 [==============================] - 0s - loss: 9.6413 - dense_1_loss_1: 3.8988 - dense_1_loss_2: 1.9009 - dense_1_loss_3: 0.8074 - dense_1_loss_4: 0.3157 - dense_1_loss_5: 0.2175 - dense_1_loss_6: 0.1629 - dense_1_loss_7: 0.1315 - dense_1_loss_8: 0.1183 - dense_1_loss_9: 0.1209 - dense_1_loss_10: 0.0982 - dense_1_loss_11: 0.1217 - dense_1_loss_12: 0.0985 - dense_1_loss_13: 0.0858 - dense_1_loss_14: 0.0892 - dense_1_loss_15: 0.0905 - dense_1_loss_16: 0.0996 - dense_1_loss_17: 0.0913 - dense_1_loss_18: 0.0969 - dense_1_loss_19: 0.0962 - dense_1_loss_20: 0.1015 - dense_1_loss_21: 0.0976 - dense_1_loss_22: 0.0956 - dense_1_loss_23: 0.0883 - dense_1_loss_24: 0.0941 - dense_1_loss_25: 0.0998 - dense_1_loss_26: 0.1024 - dense_1_loss_27: 0.1049 - dense_1_loss_28: 0.1020 - dense_1_loss_29: 0.1132 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 54/100
60/60 [==============================] - 0s - loss: 9.3907 - dense_1_loss_1: 3.8949 - dense_1_loss_2: 1.8682 - dense_1_loss_3: 0.7808 - dense_1_loss_4: 0.2993 - dense_1_loss_5: 0.2038 - dense_1_loss_6: 0.1528 - dense_1_loss_7: 0.1223 - dense_1_loss_8: 0.1109 - dense_1_loss_9: 0.1128 - dense_1_loss_10: 0.0922 - dense_1_loss_11: 0.1117 - dense_1_loss_12: 0.0917 - dense_1_loss_13: 0.0812 - dense_1_loss_14: 0.0828 - dense_1_loss_15: 0.0847 - dense_1_loss_16: 0.0917 - dense_1_loss_17: 0.0864 - dense_1_loss_18: 0.0910 - dense_1_loss_19: 0.0907 - dense_1_loss_20: 0.0946 - dense_1_loss_21: 0.0928 - dense_1_loss_22: 0.0896 - dense_1_loss_23: 0.0828 - dense_1_loss_24: 0.0875 - dense_1_loss_25: 0.0946 - dense_1_loss_26: 0.0962 - dense_1_loss_27: 0.0990 - dense_1_loss_28: 0.0964 - dense_1_loss_29: 0.1073 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 55/100
60/60 [==============================] - 0s - loss: 9.1700 - dense_1_loss_1: 3.8907 - dense_1_loss_2: 1.8363 - dense_1_loss_3: 0.7546 - dense_1_loss_4: 0.2852 - dense_1_loss_5: 0.1936 - dense_1_loss_6: 0.1452 - dense_1_loss_7: 0.1156 - dense_1_loss_8: 0.1055 - dense_1_loss_9: 0.1059 - dense_1_loss_10: 0.0872 - dense_1_loss_11: 0.1047 - dense_1_loss_12: 0.0859 - dense_1_loss_13: 0.0778 - dense_1_loss_14: 0.0783 - dense_1_loss_15: 0.0803 - dense_1_loss_16: 0.0847 - dense_1_loss_17: 0.0816 - dense_1_loss_18: 0.0855 - dense_1_loss_19: 0.0857 - dense_1_loss_20: 0.0890 - dense_1_loss_21: 0.0878 - dense_1_loss_22: 0.0842 - dense_1_loss_23: 0.0775 - dense_1_loss_24: 0.0826 - dense_1_loss_25: 0.0891 - dense_1_loss_26: 0.0905 - dense_1_loss_27: 0.0923 - dense_1_loss_28: 0.0907 - dense_1_loss_29: 0.1019 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 56/100
60/60 [==============================] - 0s - loss: 8.9552 - dense_1_loss_1: 3.8870 - dense_1_loss_2: 1.8067 - dense_1_loss_3: 0.7300 - dense_1_loss_4: 0.2717 - dense_1_loss_5: 0.1820 - dense_1_loss_6: 0.1361 - dense_1_loss_7: 0.1085 - dense_1_loss_8: 0.0994 - dense_1_loss_9: 0.1000 - dense_1_loss_10: 0.0824 - dense_1_loss_11: 0.0975 - dense_1_loss_12: 0.0807 - dense_1_loss_13: 0.0737 - dense_1_loss_14: 0.0736 - dense_1_loss_15: 0.0765 - dense_1_loss_16: 0.0803 - dense_1_loss_17: 0.0765 - dense_1_loss_18: 0.0808 - dense_1_loss_19: 0.0802 - dense_1_loss_20: 0.0840 - dense_1_loss_21: 0.0820 - dense_1_loss_22: 0.0786 - dense_1_loss_23: 0.0731 - dense_1_loss_24: 0.0779 - dense_1_loss_25: 0.0836 - dense_1_loss_26: 0.0854 - dense_1_loss_27: 0.0860 - dense_1_loss_28: 0.0851 - dense_1_loss_29: 0.0961 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 57/100
60/60 [==============================] - 0s - loss: 8.7656 - dense_1_loss_1: 3.8829 - dense_1_loss_2: 1.7771 - dense_1_loss_3: 0.7076 - dense_1_loss_4: 0.2592 - dense_1_loss_5: 0.1730 - dense_1_loss_6: 0.1292 - dense_1_loss_7: 0.1029 - dense_1_loss_8: 0.0938 - dense_1_loss_9: 0.0948 - dense_1_loss_10: 0.0779 - dense_1_loss_11: 0.0924 - dense_1_loss_12: 0.0768 - dense_1_loss_13: 0.0692 - dense_1_loss_14: 0.0702 - dense_1_loss_15: 0.0724 - dense_1_loss_16: 0.0764 - dense_1_loss_17: 0.0715 - dense_1_loss_18: 0.0761 - dense_1_loss_19: 0.0757 - dense_1_loss_20: 0.0800 - dense_1_loss_21: 0.0770 - dense_1_loss_22: 0.0744 - dense_1_loss_23: 0.0689 - dense_1_loss_24: 0.0736 - dense_1_loss_25: 0.0784 - dense_1_loss_26: 0.0809 - dense_1_loss_27: 0.0813 - dense_1_loss_28: 0.0808 - dense_1_loss_29: 0.0913 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 58/100
60/60 [==============================] - 0s - loss: 8.5852 - dense_1_loss_1: 3.8788 - dense_1_loss_2: 1.7489 - dense_1_loss_3: 0.6874 - dense_1_loss_4: 0.2468 - dense_1_loss_5: 0.1641 - dense_1_loss_6: 0.1224 - dense_1_loss_7: 0.0973 - dense_1_loss_8: 0.0889 - dense_1_loss_9: 0.0898 - dense_1_loss_10: 0.0735 - dense_1_loss_11: 0.0869 - dense_1_loss_12: 0.0727 - dense_1_loss_13: 0.0652 - dense_1_loss_14: 0.0666 - dense_1_loss_15: 0.0680 - dense_1_loss_16: 0.0722 - dense_1_loss_17: 0.0677 - dense_1_loss_18: 0.0715 - dense_1_loss_19: 0.0714 - dense_1_loss_20: 0.0754 - dense_1_loss_21: 0.0734 - dense_1_loss_22: 0.0703 - dense_1_loss_23: 0.0652 - dense_1_loss_24: 0.0694 - dense_1_loss_25: 0.0746 - dense_1_loss_26: 0.0765 - dense_1_loss_27: 0.0774 - dense_1_loss_28: 0.0768 - dense_1_loss_29: 0.0864 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.8667 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 59/100
60/60 [==============================] - 0s - loss: 8.4182 - dense_1_loss_1: 3.8753 - dense_1_loss_2: 1.7218 - dense_1_loss_3: 0.6659 - dense_1_loss_4: 0.2361 - dense_1_loss_5: 0.1560 - dense_1_loss_6: 0.1158 - dense_1_loss_7: 0.0921 - dense_1_loss_8: 0.0846 - dense_1_loss_9: 0.0852 - dense_1_loss_10: 0.0694 - dense_1_loss_11: 0.0823 - dense_1_loss_12: 0.0686 - dense_1_loss_13: 0.0617 - dense_1_loss_14: 0.0633 - dense_1_loss_15: 0.0643 - dense_1_loss_16: 0.0684 - dense_1_loss_17: 0.0642 - dense_1_loss_18: 0.0678 - dense_1_loss_19: 0.0676 - dense_1_loss_20: 0.0711 - dense_1_loss_21: 0.0699 - dense_1_loss_22: 0.0668 - dense_1_loss_23: 0.0620 - dense_1_loss_24: 0.0657 - dense_1_loss_25: 0.0711 - dense_1_loss_26: 0.0724 - dense_1_loss_27: 0.0740 - dense_1_loss_28: 0.0728 - dense_1_loss_29: 0.0822 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 0.9833 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 60/100
60/60 [==============================] - 0s - loss: 8.2606 - dense_1_loss_1: 3.8712 - dense_1_loss_2: 1.6948 - dense_1_loss_3: 0.6471 - dense_1_loss_4: 0.2261 - dense_1_loss_5: 0.1480 - dense_1_loss_6: 0.1094 - dense_1_loss_7: 0.0876 - dense_1_loss_8: 0.0806 - dense_1_loss_9: 0.0807 - dense_1_loss_10: 0.0658 - dense_1_loss_11: 0.0780 - dense_1_loss_12: 0.0651 - dense_1_loss_13: 0.0584 - dense_1_loss_14: 0.0601 - dense_1_loss_15: 0.0614 - dense_1_loss_16: 0.0647 - dense_1_loss_17: 0.0612 - dense_1_loss_18: 0.0639 - dense_1_loss_19: 0.0644 - dense_1_loss_20: 0.0675 - dense_1_loss_21: 0.0661 - dense_1_loss_22: 0.0635 - dense_1_loss_23: 0.0591 - dense_1_loss_24: 0.0621 - dense_1_loss_25: 0.0673 - dense_1_loss_26: 0.0688 - dense_1_loss_27: 0.0704 - dense_1_loss_28: 0.0693 - dense_1_loss_29: 0.0782 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 61/100
60/60 [==============================] - 0s - loss: 8.1172 - dense_1_loss_1: 3.8671 - dense_1_loss_2: 1.6703 - dense_1_loss_3: 0.6284 - dense_1_loss_4: 0.2163 - dense_1_loss_5: 0.1416 - dense_1_loss_6: 0.1045 - dense_1_loss_7: 0.0838 - dense_1_loss_8: 0.0768 - dense_1_loss_9: 0.0771 - dense_1_loss_10: 0.0630 - dense_1_loss_11: 0.0745 - dense_1_loss_12: 0.0622 - dense_1_loss_13: 0.0553 - dense_1_loss_14: 0.0572 - dense_1_loss_15: 0.0589 - dense_1_loss_16: 0.0619 - dense_1_loss_17: 0.0581 - dense_1_loss_18: 0.0606 - dense_1_loss_19: 0.0613 - dense_1_loss_20: 0.0642 - dense_1_loss_21: 0.0624 - dense_1_loss_22: 0.0603 - dense_1_loss_23: 0.0566 - dense_1_loss_24: 0.0592 - dense_1_loss_25: 0.0635 - dense_1_loss_26: 0.0654 - dense_1_loss_27: 0.0667 - dense_1_loss_28: 0.0659 - dense_1_loss_29: 0.0740 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.5667 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 62/100
60/60 [==============================] - 0s - loss: 7.9837 - dense_1_loss_1: 3.8632 - dense_1_loss_2: 1.6452 - dense_1_loss_3: 0.6116 - dense_1_loss_4: 0.2086 - dense_1_loss_5: 0.1352 - dense_1_loss_6: 0.0993 - dense_1_loss_7: 0.0800 - dense_1_loss_8: 0.0732 - dense_1_loss_9: 0.0733 - dense_1_loss_10: 0.0605 - dense_1_loss_11: 0.0707 - dense_1_loss_12: 0.0595 - dense_1_loss_13: 0.0527 - dense_1_loss_14: 0.0546 - dense_1_loss_15: 0.0564 - dense_1_loss_16: 0.0589 - dense_1_loss_17: 0.0552 - dense_1_loss_18: 0.0579 - dense_1_loss_19: 0.0584 - dense_1_loss_20: 0.0612 - dense_1_loss_21: 0.0596 - dense_1_loss_22: 0.0575 - dense_1_loss_23: 0.0538 - dense_1_loss_24: 0.0567 - dense_1_loss_25: 0.0602 - dense_1_loss_26: 0.0629 - dense_1_loss_27: 0.0634 - dense_1_loss_28: 0.0630 - dense_1_loss_29: 0.0710 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 63/100
60/60 [==============================] - 0s - loss: 7.8538 - dense_1_loss_1: 3.8596 - dense_1_loss_2: 1.6218 - dense_1_loss_3: 0.5936 - dense_1_loss_4: 0.1999 - dense_1_loss_5: 0.1288 - dense_1_loss_6: 0.0946 - dense_1_loss_7: 0.0762 - dense_1_loss_8: 0.0702 - dense_1_loss_9: 0.0698 - dense_1_loss_10: 0.0576 - dense_1_loss_11: 0.0669 - dense_1_loss_12: 0.0567 - dense_1_loss_13: 0.0505 - dense_1_loss_14: 0.0523 - dense_1_loss_15: 0.0535 - dense_1_loss_16: 0.0560 - dense_1_loss_17: 0.0527 - dense_1_loss_18: 0.0553 - dense_1_loss_19: 0.0557 - dense_1_loss_20: 0.0581 - dense_1_loss_21: 0.0572 - dense_1_loss_22: 0.0549 - dense_1_loss_23: 0.0509 - dense_1_loss_24: 0.0541 - dense_1_loss_25: 0.0576 - dense_1_loss_26: 0.0599 - dense_1_loss_27: 0.0604 - dense_1_loss_28: 0.0604 - dense_1_loss_29: 0.0686 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 64/100
60/60 [==============================] - 0s - loss: 7.7356 - dense_1_loss_1: 3.8556 - dense_1_loss_2: 1.5997 - dense_1_loss_3: 0.5772 - dense_1_loss_4: 0.1923 - dense_1_loss_5: 0.1228 - dense_1_loss_6: 0.0903 - dense_1_loss_7: 0.0727 - dense_1_loss_8: 0.0673 - dense_1_loss_9: 0.0668 - dense_1_loss_10: 0.0551 - dense_1_loss_11: 0.0635 - dense_1_loss_12: 0.0540 - dense_1_loss_13: 0.0484 - dense_1_loss_14: 0.0504 - dense_1_loss_15: 0.0509 - dense_1_loss_16: 0.0534 - dense_1_loss_17: 0.0505 - dense_1_loss_18: 0.0530 - dense_1_loss_19: 0.0532 - dense_1_loss_20: 0.0555 - dense_1_loss_21: 0.0551 - dense_1_loss_22: 0.0528 - dense_1_loss_23: 0.0485 - dense_1_loss_24: 0.0519 - dense_1_loss_25: 0.0551 - dense_1_loss_26: 0.0577 - dense_1_loss_27: 0.0577 - dense_1_loss_28: 0.0581 - dense_1_loss_29: 0.0660 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 65/100
60/60 [==============================] - 0s - loss: 7.6240 - dense_1_loss_1: 3.8520 - dense_1_loss_2: 1.5769 - dense_1_loss_3: 0.5630 - dense_1_loss_4: 0.1855 - dense_1_loss_5: 0.1175 - dense_1_loss_6: 0.0865 - dense_1_loss_7: 0.0699 - dense_1_loss_8: 0.0648 - dense_1_loss_9: 0.0640 - dense_1_loss_10: 0.0528 - dense_1_loss_11: 0.0608 - dense_1_loss_12: 0.0518 - dense_1_loss_13: 0.0464 - dense_1_loss_14: 0.0483 - dense_1_loss_15: 0.0487 - dense_1_loss_16: 0.0515 - dense_1_loss_17: 0.0482 - dense_1_loss_18: 0.0509 - dense_1_loss_19: 0.0507 - dense_1_loss_20: 0.0534 - dense_1_loss_21: 0.0525 - dense_1_loss_22: 0.0505 - dense_1_loss_23: 0.0464 - dense_1_loss_24: 0.0496 - dense_1_loss_25: 0.0527 - dense_1_loss_26: 0.0549 - dense_1_loss_27: 0.0552 - dense_1_loss_28: 0.0554 - dense_1_loss_29: 0.0630 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 66/100
60/60 [==============================] - 0s - loss: 7.5171 - dense_1_loss_1: 3.8482 - dense_1_loss_2: 1.5556 - dense_1_loss_3: 0.5476 - dense_1_loss_4: 0.1793 - dense_1_loss_5: 0.1124 - dense_1_loss_6: 0.0828 - dense_1_loss_7: 0.0674 - dense_1_loss_8: 0.0622 - dense_1_loss_9: 0.0613 - dense_1_loss_10: 0.0506 - dense_1_loss_11: 0.0584 - dense_1_loss_12: 0.0497 - dense_1_loss_13: 0.0444 - dense_1_loss_14: 0.0460 - dense_1_loss_15: 0.0470 - dense_1_loss_16: 0.0498 - dense_1_loss_17: 0.0460 - dense_1_loss_18: 0.0485 - dense_1_loss_19: 0.0484 - dense_1_loss_20: 0.0513 - dense_1_loss_21: 0.0503 - dense_1_loss_22: 0.0483 - dense_1_loss_23: 0.0450 - dense_1_loss_24: 0.0476 - dense_1_loss_25: 0.0506 - dense_1_loss_26: 0.0523 - dense_1_loss_27: 0.0530 - dense_1_loss_28: 0.0531 - dense_1_loss_29: 0.0602 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 67/100
60/60 [==============================] - 0s - loss: 7.4190 - dense_1_loss_1: 3.8443 - dense_1_loss_2: 1.5344 - dense_1_loss_3: 0.5345 - dense_1_loss_4: 0.1730 - dense_1_loss_5: 0.1083 - dense_1_loss_6: 0.0795 - dense_1_loss_7: 0.0652 - dense_1_loss_8: 0.0597 - dense_1_loss_9: 0.0591 - dense_1_loss_10: 0.0488 - dense_1_loss_11: 0.0562 - dense_1_loss_12: 0.0478 - dense_1_loss_13: 0.0426 - dense_1_loss_14: 0.0441 - dense_1_loss_15: 0.0453 - dense_1_loss_16: 0.0479 - dense_1_loss_17: 0.0440 - dense_1_loss_18: 0.0465 - dense_1_loss_19: 0.0464 - dense_1_loss_20: 0.0493 - dense_1_loss_21: 0.0482 - dense_1_loss_22: 0.0464 - dense_1_loss_23: 0.0434 - dense_1_loss_24: 0.0457 - dense_1_loss_25: 0.0485 - dense_1_loss_26: 0.0504 - dense_1_loss_27: 0.0512 - dense_1_loss_28: 0.0511 - dense_1_loss_29: 0.0571 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 68/100
60/60 [==============================] - 0s - loss: 7.3250 - dense_1_loss_1: 3.8406 - dense_1_loss_2: 1.5141 - dense_1_loss_3: 0.5213 - dense_1_loss_4: 0.1672 - dense_1_loss_5: 0.1041 - dense_1_loss_6: 0.0763 - dense_1_loss_7: 0.0628 - dense_1_loss_8: 0.0575 - dense_1_loss_9: 0.0569 - dense_1_loss_10: 0.0469 - dense_1_loss_11: 0.0541 - dense_1_loss_12: 0.0460 - dense_1_loss_13: 0.0409 - dense_1_loss_14: 0.0425 - dense_1_loss_15: 0.0436 - dense_1_loss_16: 0.0458 - dense_1_loss_17: 0.0423 - dense_1_loss_18: 0.0446 - dense_1_loss_19: 0.0447 - dense_1_loss_20: 0.0474 - dense_1_loss_21: 0.0465 - dense_1_loss_22: 0.0446 - dense_1_loss_23: 0.0416 - dense_1_loss_24: 0.0438 - dense_1_loss_25: 0.0466 - dense_1_loss_26: 0.0486 - dense_1_loss_27: 0.0493 - dense_1_loss_28: 0.0491 - dense_1_loss_29: 0.0550 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 69/100
60/60 [==============================] - 0s - loss: 7.2364 - dense_1_loss_1: 3.8370 - dense_1_loss_2: 1.4951 - dense_1_loss_3: 0.5087 - dense_1_loss_4: 0.1615 - dense_1_loss_5: 0.1001 - dense_1_loss_6: 0.0732 - dense_1_loss_7: 0.0605 - dense_1_loss_8: 0.0554 - dense_1_loss_9: 0.0549 - dense_1_loss_10: 0.0453 - dense_1_loss_11: 0.0519 - dense_1_loss_12: 0.0442 - dense_1_loss_13: 0.0395 - dense_1_loss_14: 0.0410 - dense_1_loss_15: 0.0421 - dense_1_loss_16: 0.0439 - dense_1_loss_17: 0.0407 - dense_1_loss_18: 0.0431 - dense_1_loss_19: 0.0431 - dense_1_loss_20: 0.0455 - dense_1_loss_21: 0.0451 - dense_1_loss_22: 0.0428 - dense_1_loss_23: 0.0399 - dense_1_loss_24: 0.0423 - dense_1_loss_25: 0.0448 - dense_1_loss_26: 0.0469 - dense_1_loss_27: 0.0473 - dense_1_loss_28: 0.0475 - dense_1_loss_29: 0.0532 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 70/100
60/60 [==============================] - 0s - loss: 7.1507 - dense_1_loss_1: 3.8333 - dense_1_loss_2: 1.4752 - dense_1_loss_3: 0.4963 - dense_1_loss_4: 0.1564 - dense_1_loss_5: 0.0965 - dense_1_loss_6: 0.0703 - dense_1_loss_7: 0.0584 - dense_1_loss_8: 0.0535 - dense_1_loss_9: 0.0529 - dense_1_loss_10: 0.0436 - dense_1_loss_11: 0.0500 - dense_1_loss_12: 0.0426 - dense_1_loss_13: 0.0380 - dense_1_loss_14: 0.0396 - dense_1_loss_15: 0.0406 - dense_1_loss_16: 0.0423 - dense_1_loss_17: 0.0393 - dense_1_loss_18: 0.0414 - dense_1_loss_19: 0.0416 - dense_1_loss_20: 0.0439 - dense_1_loss_21: 0.0434 - dense_1_loss_22: 0.0413 - dense_1_loss_23: 0.0384 - dense_1_loss_24: 0.0408 - dense_1_loss_25: 0.0430 - dense_1_loss_26: 0.0453 - dense_1_loss_27: 0.0456 - dense_1_loss_28: 0.0457 - dense_1_loss_29: 0.0516 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9000 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 71/100
60/60 [==============================] - 0s - loss: 7.0698 - dense_1_loss_1: 3.8295 - dense_1_loss_2: 1.4567 - dense_1_loss_3: 0.4849 - dense_1_loss_4: 0.1514 - dense_1_loss_5: 0.0930 - dense_1_loss_6: 0.0678 - dense_1_loss_7: 0.0565 - dense_1_loss_8: 0.0517 - dense_1_loss_9: 0.0512 - dense_1_loss_10: 0.0422 - dense_1_loss_11: 0.0482 - dense_1_loss_12: 0.0412 - dense_1_loss_13: 0.0365 - dense_1_loss_14: 0.0381 - dense_1_loss_15: 0.0393 - dense_1_loss_16: 0.0409 - dense_1_loss_17: 0.0378 - dense_1_loss_18: 0.0399 - dense_1_loss_19: 0.0400 - dense_1_loss_20: 0.0423 - dense_1_loss_21: 0.0417 - dense_1_loss_22: 0.0398 - dense_1_loss_23: 0.0371 - dense_1_loss_24: 0.0394 - dense_1_loss_25: 0.0415 - dense_1_loss_26: 0.0438 - dense_1_loss_27: 0.0437 - dense_1_loss_28: 0.0439 - dense_1_loss_29: 0.0500 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 72/100
60/60 [==============================] - 0s - loss: 6.9929 - dense_1_loss_1: 3.8260 - dense_1_loss_2: 1.4383 - dense_1_loss_3: 0.4737 - dense_1_loss_4: 0.1473 - dense_1_loss_5: 0.0899 - dense_1_loss_6: 0.0654 - dense_1_loss_7: 0.0546 - dense_1_loss_8: 0.0499 - dense_1_loss_9: 0.0495 - dense_1_loss_10: 0.0407 - dense_1_loss_11: 0.0464 - dense_1_loss_12: 0.0398 - dense_1_loss_13: 0.0352 - dense_1_loss_14: 0.0367 - dense_1_loss_15: 0.0380 - dense_1_loss_16: 0.0395 - dense_1_loss_17: 0.0365 - dense_1_loss_18: 0.0384 - dense_1_loss_19: 0.0386 - dense_1_loss_20: 0.0409 - dense_1_loss_21: 0.0402 - dense_1_loss_22: 0.0384 - dense_1_loss_23: 0.0360 - dense_1_loss_24: 0.0381 - dense_1_loss_25: 0.0400 - dense_1_loss_26: 0.0422 - dense_1_loss_27: 0.0420 - dense_1_loss_28: 0.0424 - dense_1_loss_29: 0.0484 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 73/100
60/60 [==============================] - 0s - loss: 6.9210 - dense_1_loss_1: 3.8220 - dense_1_loss_2: 1.4211 - dense_1_loss_3: 0.4635 - dense_1_loss_4: 0.1428 - dense_1_loss_5: 0.0870 - dense_1_loss_6: 0.0634 - dense_1_loss_7: 0.0529 - dense_1_loss_8: 0.0483 - dense_1_loss_9: 0.0479 - dense_1_loss_10: 0.0394 - dense_1_loss_11: 0.0449 - dense_1_loss_12: 0.0385 - dense_1_loss_13: 0.0341 - dense_1_loss_14: 0.0356 - dense_1_loss_15: 0.0367 - dense_1_loss_16: 0.0381 - dense_1_loss_17: 0.0353 - dense_1_loss_18: 0.0370 - dense_1_loss_19: 0.0374 - dense_1_loss_20: 0.0395 - dense_1_loss_21: 0.0388 - dense_1_loss_22: 0.0371 - dense_1_loss_23: 0.0349 - dense_1_loss_24: 0.0369 - dense_1_loss_25: 0.0387 - dense_1_loss_26: 0.0407 - dense_1_loss_27: 0.0407 - dense_1_loss_28: 0.0410 - dense_1_loss_29: 0.0468 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 74/100
60/60 [==============================] - 0s - loss: 6.8496 - dense_1_loss_1: 3.8183 - dense_1_loss_2: 1.4044 - dense_1_loss_3: 0.4524 - dense_1_loss_4: 0.1384 - dense_1_loss_5: 0.0840 - dense_1_loss_6: 0.0611 - dense_1_loss_7: 0.0511 - dense_1_loss_8: 0.0468 - dense_1_loss_9: 0.0464 - dense_1_loss_10: 0.0380 - dense_1_loss_11: 0.0433 - dense_1_loss_12: 0.0372 - dense_1_loss_13: 0.0330 - dense_1_loss_14: 0.0345 - dense_1_loss_15: 0.0355 - dense_1_loss_16: 0.0368 - dense_1_loss_17: 0.0342 - dense_1_loss_18: 0.0358 - dense_1_loss_19: 0.0361 - dense_1_loss_20: 0.0381 - dense_1_loss_21: 0.0377 - dense_1_loss_22: 0.0358 - dense_1_loss_23: 0.0337 - dense_1_loss_24: 0.0357 - dense_1_loss_25: 0.0375 - dense_1_loss_26: 0.0395 - dense_1_loss_27: 0.0395 - dense_1_loss_28: 0.0396 - dense_1_loss_29: 0.0453 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 75/100
60/60 [==============================] - 0s - loss: 6.7845 - dense_1_loss_1: 3.8150 - dense_1_loss_2: 1.3878 - dense_1_loss_3: 0.4426 - dense_1_loss_4: 0.1346 - dense_1_loss_5: 0.0814 - dense_1_loss_6: 0.0592 - dense_1_loss_7: 0.0496 - dense_1_loss_8: 0.0454 - dense_1_loss_9: 0.0449 - dense_1_loss_10: 0.0369 - dense_1_loss_11: 0.0420 - dense_1_loss_12: 0.0360 - dense_1_loss_13: 0.0321 - dense_1_loss_14: 0.0334 - dense_1_loss_15: 0.0343 - dense_1_loss_16: 0.0356 - dense_1_loss_17: 0.0331 - dense_1_loss_18: 0.0347 - dense_1_loss_19: 0.0350 - dense_1_loss_20: 0.0369 - dense_1_loss_21: 0.0366 - dense_1_loss_22: 0.0347 - dense_1_loss_23: 0.0325 - dense_1_loss_24: 0.0345 - dense_1_loss_25: 0.0365 - dense_1_loss_26: 0.0382 - dense_1_loss_27: 0.0383 - dense_1_loss_28: 0.0385 - dense_1_loss_29: 0.0440 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 76/100
60/60 [==============================] - 0s - loss: 6.7198 - dense_1_loss_1: 3.8113 - dense_1_loss_2: 1.3713 - dense_1_loss_3: 0.4330 - dense_1_loss_4: 0.1308 - dense_1_loss_5: 0.0790 - dense_1_loss_6: 0.0572 - dense_1_loss_7: 0.0483 - dense_1_loss_8: 0.0442 - dense_1_loss_9: 0.0436 - dense_1_loss_10: 0.0358 - dense_1_loss_11: 0.0408 - dense_1_loss_12: 0.0349 - dense_1_loss_13: 0.0312 - dense_1_loss_14: 0.0324 - dense_1_loss_15: 0.0333 - dense_1_loss_16: 0.0345 - dense_1_loss_17: 0.0320 - dense_1_loss_18: 0.0336 - dense_1_loss_19: 0.0339 - dense_1_loss_20: 0.0358 - dense_1_loss_21: 0.0355 - dense_1_loss_22: 0.0336 - dense_1_loss_23: 0.0315 - dense_1_loss_24: 0.0335 - dense_1_loss_25: 0.0354 - dense_1_loss_26: 0.0368 - dense_1_loss_27: 0.0371 - dense_1_loss_28: 0.0374 - dense_1_loss_29: 0.0423 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 77/100
60/60 [==============================] - 0s - loss: 6.6606 - dense_1_loss_1: 3.8078 - dense_1_loss_2: 1.3562 - dense_1_loss_3: 0.4239 - dense_1_loss_4: 0.1276 - dense_1_loss_5: 0.0769 - dense_1_loss_6: 0.0555 - dense_1_loss_7: 0.0470 - dense_1_loss_8: 0.0430 - dense_1_loss_9: 0.0423 - dense_1_loss_10: 0.0348 - dense_1_loss_11: 0.0396 - dense_1_loss_12: 0.0339 - dense_1_loss_13: 0.0303 - dense_1_loss_14: 0.0314 - dense_1_loss_15: 0.0324 - dense_1_loss_16: 0.0335 - dense_1_loss_17: 0.0311 - dense_1_loss_18: 0.0326 - dense_1_loss_19: 0.0329 - dense_1_loss_20: 0.0347 - dense_1_loss_21: 0.0344 - dense_1_loss_22: 0.0326 - dense_1_loss_23: 0.0306 - dense_1_loss_24: 0.0324 - dense_1_loss_25: 0.0342 - dense_1_loss_26: 0.0358 - dense_1_loss_27: 0.0361 - dense_1_loss_28: 0.0362 - dense_1_loss_29: 0.0409 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 78/100
60/60 [==============================] - 0s - loss: 6.6013 - dense_1_loss_1: 3.8040 - dense_1_loss_2: 1.3404 - dense_1_loss_3: 0.4153 - dense_1_loss_4: 0.1241 - dense_1_loss_5: 0.0745 - dense_1_loss_6: 0.0538 - dense_1_loss_7: 0.0457 - dense_1_loss_8: 0.0417 - dense_1_loss_9: 0.0411 - dense_1_loss_10: 0.0339 - dense_1_loss_11: 0.0384 - dense_1_loss_12: 0.0328 - dense_1_loss_13: 0.0293 - dense_1_loss_14: 0.0304 - dense_1_loss_15: 0.0314 - dense_1_loss_16: 0.0325 - dense_1_loss_17: 0.0302 - dense_1_loss_18: 0.0316 - dense_1_loss_19: 0.0319 - dense_1_loss_20: 0.0336 - dense_1_loss_21: 0.0334 - dense_1_loss_22: 0.0317 - dense_1_loss_23: 0.0299 - dense_1_loss_24: 0.0315 - dense_1_loss_25: 0.0332 - dense_1_loss_26: 0.0349 - dense_1_loss_27: 0.0351 - dense_1_loss_28: 0.0352 - dense_1_loss_29: 0.0397 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 79/100
60/60 [==============================] - 0s - loss: 6.5439 - dense_1_loss_1: 3.8008 - dense_1_loss_2: 1.3256 - dense_1_loss_3: 0.4060 - dense_1_loss_4: 0.1206 - dense_1_loss_5: 0.0721 - dense_1_loss_6: 0.0522 - dense_1_loss_7: 0.0444 - dense_1_loss_8: 0.0405 - dense_1_loss_9: 0.0399 - dense_1_loss_10: 0.0330 - dense_1_loss_11: 0.0372 - dense_1_loss_12: 0.0318 - dense_1_loss_13: 0.0285 - dense_1_loss_14: 0.0296 - dense_1_loss_15: 0.0305 - dense_1_loss_16: 0.0316 - dense_1_loss_17: 0.0293 - dense_1_loss_18: 0.0307 - dense_1_loss_19: 0.0310 - dense_1_loss_20: 0.0327 - dense_1_loss_21: 0.0324 - dense_1_loss_22: 0.0308 - dense_1_loss_23: 0.0291 - dense_1_loss_24: 0.0307 - dense_1_loss_25: 0.0323 - dense_1_loss_26: 0.0339 - dense_1_loss_27: 0.0341 - dense_1_loss_28: 0.0341 - dense_1_loss_29: 0.0386 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 80/100
60/60 [==============================] - 0s - loss: 6.4905 - dense_1_loss_1: 3.7971 - dense_1_loss_2: 1.3110 - dense_1_loss_3: 0.3981 - dense_1_loss_4: 0.1179 - dense_1_loss_5: 0.0702 - dense_1_loss_6: 0.0508 - dense_1_loss_7: 0.0432 - dense_1_loss_8: 0.0395 - dense_1_loss_9: 0.0389 - dense_1_loss_10: 0.0322 - dense_1_loss_11: 0.0362 - dense_1_loss_12: 0.0309 - dense_1_loss_13: 0.0277 - dense_1_loss_14: 0.0287 - dense_1_loss_15: 0.0296 - dense_1_loss_16: 0.0307 - dense_1_loss_17: 0.0285 - dense_1_loss_18: 0.0298 - dense_1_loss_19: 0.0301 - dense_1_loss_20: 0.0317 - dense_1_loss_21: 0.0315 - dense_1_loss_22: 0.0300 - dense_1_loss_23: 0.0283 - dense_1_loss_24: 0.0298 - dense_1_loss_25: 0.0314 - dense_1_loss_26: 0.0329 - dense_1_loss_27: 0.0332 - dense_1_loss_28: 0.0331 - dense_1_loss_29: 0.0374 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9167 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 81/100
60/60 [==============================] - 0s - loss: 6.4382 - dense_1_loss_1: 3.7937 - dense_1_loss_2: 1.2969 - dense_1_loss_3: 0.3898 - dense_1_loss_4: 0.1149 - dense_1_loss_5: 0.0681 - dense_1_loss_6: 0.0493 - dense_1_loss_7: 0.0420 - dense_1_loss_8: 0.0384 - dense_1_loss_9: 0.0378 - dense_1_loss_10: 0.0314 - dense_1_loss_11: 0.0352 - dense_1_loss_12: 0.0301 - dense_1_loss_13: 0.0269 - dense_1_loss_14: 0.0280 - dense_1_loss_15: 0.0288 - dense_1_loss_16: 0.0297 - dense_1_loss_17: 0.0277 - dense_1_loss_18: 0.0290 - dense_1_loss_19: 0.0293 - dense_1_loss_20: 0.0309 - dense_1_loss_21: 0.0306 - dense_1_loss_22: 0.0293 - dense_1_loss_23: 0.0274 - dense_1_loss_24: 0.0290 - dense_1_loss_25: 0.0305 - dense_1_loss_26: 0.0321 - dense_1_loss_27: 0.0323 - dense_1_loss_28: 0.0323 - dense_1_loss_29: 0.0365 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 82/100
60/60 [==============================] - 0s - loss: 6.3889 - dense_1_loss_1: 3.7903 - dense_1_loss_2: 1.2832 - dense_1_loss_3: 0.3825 - dense_1_loss_4: 0.1120 - dense_1_loss_5: 0.0665 - dense_1_loss_6: 0.0481 - dense_1_loss_7: 0.0410 - dense_1_loss_8: 0.0375 - dense_1_loss_9: 0.0368 - dense_1_loss_10: 0.0306 - dense_1_loss_11: 0.0343 - dense_1_loss_12: 0.0293 - dense_1_loss_13: 0.0263 - dense_1_loss_14: 0.0273 - dense_1_loss_15: 0.0281 - dense_1_loss_16: 0.0290 - dense_1_loss_17: 0.0270 - dense_1_loss_18: 0.0282 - dense_1_loss_19: 0.0285 - dense_1_loss_20: 0.0301 - dense_1_loss_21: 0.0298 - dense_1_loss_22: 0.0285 - dense_1_loss_23: 0.0266 - dense_1_loss_24: 0.0283 - dense_1_loss_25: 0.0297 - dense_1_loss_26: 0.0312 - dense_1_loss_27: 0.0315 - dense_1_loss_28: 0.0315 - dense_1_loss_29: 0.0355 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6167 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 83/100
60/60 [==============================] - 0s - loss: 6.3391 - dense_1_loss_1: 3.7868 - dense_1_loss_2: 1.2691 - dense_1_loss_3: 0.3746 - dense_1_loss_4: 0.1093 - dense_1_loss_5: 0.0648 - dense_1_loss_6: 0.0467 - dense_1_loss_7: 0.0400 - dense_1_loss_8: 0.0364 - dense_1_loss_9: 0.0358 - dense_1_loss_10: 0.0298 - dense_1_loss_11: 0.0333 - dense_1_loss_12: 0.0285 - dense_1_loss_13: 0.0256 - dense_1_loss_14: 0.0265 - dense_1_loss_15: 0.0274 - dense_1_loss_16: 0.0282 - dense_1_loss_17: 0.0263 - dense_1_loss_18: 0.0275 - dense_1_loss_19: 0.0278 - dense_1_loss_20: 0.0293 - dense_1_loss_21: 0.0290 - dense_1_loss_22: 0.0277 - dense_1_loss_23: 0.0258 - dense_1_loss_24: 0.0276 - dense_1_loss_25: 0.0289 - dense_1_loss_26: 0.0304 - dense_1_loss_27: 0.0307 - dense_1_loss_28: 0.0307 - dense_1_loss_29: 0.0346 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6500 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 84/100
60/60 [==============================] - 0s - loss: 6.2930 - dense_1_loss_1: 3.7834 - dense_1_loss_2: 1.2556 - dense_1_loss_3: 0.3679 - dense_1_loss_4: 0.1068 - dense_1_loss_5: 0.0632 - dense_1_loss_6: 0.0454 - dense_1_loss_7: 0.0391 - dense_1_loss_8: 0.0355 - dense_1_loss_9: 0.0349 - dense_1_loss_10: 0.0291 - dense_1_loss_11: 0.0325 - dense_1_loss_12: 0.0278 - dense_1_loss_13: 0.0249 - dense_1_loss_14: 0.0258 - dense_1_loss_15: 0.0267 - dense_1_loss_16: 0.0275 - dense_1_loss_17: 0.0256 - dense_1_loss_18: 0.0268 - dense_1_loss_19: 0.0270 - dense_1_loss_20: 0.0285 - dense_1_loss_21: 0.0282 - dense_1_loss_22: 0.0270 - dense_1_loss_23: 0.0252 - dense_1_loss_24: 0.0269 - dense_1_loss_25: 0.0281 - dense_1_loss_26: 0.0297 - dense_1_loss_27: 0.0299 - dense_1_loss_28: 0.0299 - dense_1_loss_29: 0.0339 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6500 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 85/100
60/60 [==============================] - 0s - loss: 6.2478 - dense_1_loss_1: 3.7799 - dense_1_loss_2: 1.2433 - dense_1_loss_3: 0.3604 - dense_1_loss_4: 0.1043 - dense_1_loss_5: 0.0615 - dense_1_loss_6: 0.0443 - dense_1_loss_7: 0.0381 - dense_1_loss_8: 0.0346 - dense_1_loss_9: 0.0341 - dense_1_loss_10: 0.0285 - dense_1_loss_11: 0.0317 - dense_1_loss_12: 0.0271 - dense_1_loss_13: 0.0243 - dense_1_loss_14: 0.0252 - dense_1_loss_15: 0.0260 - dense_1_loss_16: 0.0268 - dense_1_loss_17: 0.0249 - dense_1_loss_18: 0.0261 - dense_1_loss_19: 0.0263 - dense_1_loss_20: 0.0278 - dense_1_loss_21: 0.0275 - dense_1_loss_22: 0.0264 - dense_1_loss_23: 0.0246 - dense_1_loss_24: 0.0262 - dense_1_loss_25: 0.0275 - dense_1_loss_26: 0.0290 - dense_1_loss_27: 0.0291 - dense_1_loss_28: 0.0291 - dense_1_loss_29: 0.0331 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6500 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 86/100
60/60 [==============================] - 0s - loss: 6.2047 - dense_1_loss_1: 3.7768 - dense_1_loss_2: 1.2299 - dense_1_loss_3: 0.3543 - dense_1_loss_4: 0.1021 - dense_1_loss_5: 0.0601 - dense_1_loss_6: 0.0433 - dense_1_loss_7: 0.0372 - dense_1_loss_8: 0.0338 - dense_1_loss_9: 0.0332 - dense_1_loss_10: 0.0279 - dense_1_loss_11: 0.0309 - dense_1_loss_12: 0.0264 - dense_1_loss_13: 0.0237 - dense_1_loss_14: 0.0246 - dense_1_loss_15: 0.0253 - dense_1_loss_16: 0.0262 - dense_1_loss_17: 0.0243 - dense_1_loss_18: 0.0255 - dense_1_loss_19: 0.0256 - dense_1_loss_20: 0.0271 - dense_1_loss_21: 0.0268 - dense_1_loss_22: 0.0257 - dense_1_loss_23: 0.0240 - dense_1_loss_24: 0.0256 - dense_1_loss_25: 0.0268 - dense_1_loss_26: 0.0283 - dense_1_loss_27: 0.0284 - dense_1_loss_28: 0.0284 - dense_1_loss_29: 0.0323 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 87/100
60/60 [==============================] - 0s - loss: 6.1634 - dense_1_loss_1: 3.7732 - dense_1_loss_2: 1.2184 - dense_1_loss_3: 0.3480 - dense_1_loss_4: 0.0998 - dense_1_loss_5: 0.0588 - dense_1_loss_6: 0.0422 - dense_1_loss_7: 0.0364 - dense_1_loss_8: 0.0331 - dense_1_loss_9: 0.0325 - dense_1_loss_10: 0.0272 - dense_1_loss_11: 0.0302 - dense_1_loss_12: 0.0258 - dense_1_loss_13: 0.0232 - dense_1_loss_14: 0.0240 - dense_1_loss_15: 0.0247 - dense_1_loss_16: 0.0255 - dense_1_loss_17: 0.0238 - dense_1_loss_18: 0.0248 - dense_1_loss_19: 0.0250 - dense_1_loss_20: 0.0265 - dense_1_loss_21: 0.0262 - dense_1_loss_22: 0.0251 - dense_1_loss_23: 0.0235 - dense_1_loss_24: 0.0250 - dense_1_loss_25: 0.0261 - dense_1_loss_26: 0.0276 - dense_1_loss_27: 0.0278 - dense_1_loss_28: 0.0277 - dense_1_loss_29: 0.0315 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 88/100
60/60 [==============================] - 0s - loss: 6.1224 - dense_1_loss_1: 3.7698 - dense_1_loss_2: 1.2063 - dense_1_loss_3: 0.3416 - dense_1_loss_4: 0.0977 - dense_1_loss_5: 0.0574 - dense_1_loss_6: 0.0412 - dense_1_loss_7: 0.0355 - dense_1_loss_8: 0.0323 - dense_1_loss_9: 0.0318 - dense_1_loss_10: 0.0266 - dense_1_loss_11: 0.0295 - dense_1_loss_12: 0.0252 - dense_1_loss_13: 0.0226 - dense_1_loss_14: 0.0234 - dense_1_loss_15: 0.0241 - dense_1_loss_16: 0.0249 - dense_1_loss_17: 0.0232 - dense_1_loss_18: 0.0243 - dense_1_loss_19: 0.0244 - dense_1_loss_20: 0.0259 - dense_1_loss_21: 0.0256 - dense_1_loss_22: 0.0244 - dense_1_loss_23: 0.0229 - dense_1_loss_24: 0.0245 - dense_1_loss_25: 0.0255 - dense_1_loss_26: 0.0269 - dense_1_loss_27: 0.0272 - dense_1_loss_28: 0.0271 - dense_1_loss_29: 0.0307 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 89/100
60/60 [==============================] - 0s - loss: 6.0826 - dense_1_loss_1: 3.7664 - dense_1_loss_2: 1.1942 - dense_1_loss_3: 0.3355 - dense_1_loss_4: 0.0955 - dense_1_loss_5: 0.0561 - dense_1_loss_6: 0.0402 - dense_1_loss_7: 0.0348 - dense_1_loss_8: 0.0316 - dense_1_loss_9: 0.0310 - dense_1_loss_10: 0.0261 - dense_1_loss_11: 0.0288 - dense_1_loss_12: 0.0246 - dense_1_loss_13: 0.0222 - dense_1_loss_14: 0.0229 - dense_1_loss_15: 0.0235 - dense_1_loss_16: 0.0243 - dense_1_loss_17: 0.0227 - dense_1_loss_18: 0.0237 - dense_1_loss_19: 0.0238 - dense_1_loss_20: 0.0253 - dense_1_loss_21: 0.0250 - dense_1_loss_22: 0.0239 - dense_1_loss_23: 0.0224 - dense_1_loss_24: 0.0239 - dense_1_loss_25: 0.0250 - dense_1_loss_26: 0.0263 - dense_1_loss_27: 0.0266 - dense_1_loss_28: 0.0265 - dense_1_loss_29: 0.0300 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 90/100
60/60 [==============================] - 0s - loss: 6.0451 - dense_1_loss_1: 3.7631 - dense_1_loss_2: 1.1831 - dense_1_loss_3: 0.3297 - dense_1_loss_4: 0.0935 - dense_1_loss_5: 0.0550 - dense_1_loss_6: 0.0392 - dense_1_loss_7: 0.0340 - dense_1_loss_8: 0.0309 - dense_1_loss_9: 0.0304 - dense_1_loss_10: 0.0255 - dense_1_loss_11: 0.0283 - dense_1_loss_12: 0.0241 - dense_1_loss_13: 0.0217 - dense_1_loss_14: 0.0224 - dense_1_loss_15: 0.0230 - dense_1_loss_16: 0.0237 - dense_1_loss_17: 0.0222 - dense_1_loss_18: 0.0231 - dense_1_loss_19: 0.0232 - dense_1_loss_20: 0.0247 - dense_1_loss_21: 0.0245 - dense_1_loss_22: 0.0234 - dense_1_loss_23: 0.0219 - dense_1_loss_24: 0.0233 - dense_1_loss_25: 0.0244 - dense_1_loss_26: 0.0256 - dense_1_loss_27: 0.0260 - dense_1_loss_28: 0.0259 - dense_1_loss_29: 0.0294 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 91/100
60/60 [==============================] - 0s - loss: 6.0091 - dense_1_loss_1: 3.7600 - dense_1_loss_2: 1.1720 - dense_1_loss_3: 0.3247 - dense_1_loss_4: 0.0915 - dense_1_loss_5: 0.0539 - dense_1_loss_6: 0.0383 - dense_1_loss_7: 0.0334 - dense_1_loss_8: 0.0303 - dense_1_loss_9: 0.0297 - dense_1_loss_10: 0.0250 - dense_1_loss_11: 0.0277 - dense_1_loss_12: 0.0235 - dense_1_loss_13: 0.0212 - dense_1_loss_14: 0.0219 - dense_1_loss_15: 0.0225 - dense_1_loss_16: 0.0232 - dense_1_loss_17: 0.0217 - dense_1_loss_18: 0.0226 - dense_1_loss_19: 0.0227 - dense_1_loss_20: 0.0242 - dense_1_loss_21: 0.0239 - dense_1_loss_22: 0.0229 - dense_1_loss_23: 0.0214 - dense_1_loss_24: 0.0228 - dense_1_loss_25: 0.0239 - dense_1_loss_26: 0.0251 - dense_1_loss_27: 0.0254 - dense_1_loss_28: 0.0253 - dense_1_loss_29: 0.0287 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 92/100
60/60 [==============================] - 0s - loss: 5.9724 - dense_1_loss_1: 3.7565 - dense_1_loss_2: 1.1609 - dense_1_loss_3: 0.3189 - dense_1_loss_4: 0.0896 - dense_1_loss_5: 0.0528 - dense_1_loss_6: 0.0374 - dense_1_loss_7: 0.0327 - dense_1_loss_8: 0.0296 - dense_1_loss_9: 0.0290 - dense_1_loss_10: 0.0244 - dense_1_loss_11: 0.0271 - dense_1_loss_12: 0.0230 - dense_1_loss_13: 0.0207 - dense_1_loss_14: 0.0214 - dense_1_loss_15: 0.0221 - dense_1_loss_16: 0.0227 - dense_1_loss_17: 0.0212 - dense_1_loss_18: 0.0221 - dense_1_loss_19: 0.0222 - dense_1_loss_20: 0.0236 - dense_1_loss_21: 0.0234 - dense_1_loss_22: 0.0224 - dense_1_loss_23: 0.0209 - dense_1_loss_24: 0.0224 - dense_1_loss_25: 0.0234 - dense_1_loss_26: 0.0245 - dense_1_loss_27: 0.0248 - dense_1_loss_28: 0.0247 - dense_1_loss_29: 0.0280 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 93/100
60/60 [==============================] - 0s - loss: 5.9388 - dense_1_loss_1: 3.7531 - dense_1_loss_2: 1.1507 - dense_1_loss_3: 0.3140 - dense_1_loss_4: 0.0879 - dense_1_loss_5: 0.0518 - dense_1_loss_6: 0.0365 - dense_1_loss_7: 0.0320 - dense_1_loss_8: 0.0290 - dense_1_loss_9: 0.0284 - dense_1_loss_10: 0.0240 - dense_1_loss_11: 0.0265 - dense_1_loss_12: 0.0225 - dense_1_loss_13: 0.0203 - dense_1_loss_14: 0.0209 - dense_1_loss_15: 0.0216 - dense_1_loss_16: 0.0222 - dense_1_loss_17: 0.0207 - dense_1_loss_18: 0.0216 - dense_1_loss_19: 0.0218 - dense_1_loss_20: 0.0231 - dense_1_loss_21: 0.0230 - dense_1_loss_22: 0.0219 - dense_1_loss_23: 0.0205 - dense_1_loss_24: 0.0219 - dense_1_loss_25: 0.0228 - dense_1_loss_26: 0.0240 - dense_1_loss_27: 0.0244 - dense_1_loss_28: 0.0243 - dense_1_loss_29: 0.0274 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 94/100
60/60 [==============================] - 0s - loss: 5.9052 - dense_1_loss_1: 3.7502 - dense_1_loss_2: 1.1397 - dense_1_loss_3: 0.3089 - dense_1_loss_4: 0.0863 - dense_1_loss_5: 0.0507 - dense_1_loss_6: 0.0358 - dense_1_loss_7: 0.0314 - dense_1_loss_8: 0.0284 - dense_1_loss_9: 0.0278 - dense_1_loss_10: 0.0235 - dense_1_loss_11: 0.0260 - dense_1_loss_12: 0.0221 - dense_1_loss_13: 0.0198 - dense_1_loss_14: 0.0205 - dense_1_loss_15: 0.0211 - dense_1_loss_16: 0.0217 - dense_1_loss_17: 0.0203 - dense_1_loss_18: 0.0212 - dense_1_loss_19: 0.0213 - dense_1_loss_20: 0.0226 - dense_1_loss_21: 0.0225 - dense_1_loss_22: 0.0214 - dense_1_loss_23: 0.0200 - dense_1_loss_24: 0.0215 - dense_1_loss_25: 0.0222 - dense_1_loss_26: 0.0235 - dense_1_loss_27: 0.0240 - dense_1_loss_28: 0.0238 - dense_1_loss_29: 0.0268 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 95/100
60/60 [==============================] - 0s - loss: 5.8725 - dense_1_loss_1: 3.7469 - dense_1_loss_2: 1.1300 - dense_1_loss_3: 0.3034 - dense_1_loss_4: 0.0847 - dense_1_loss_5: 0.0497 - dense_1_loss_6: 0.0350 - dense_1_loss_7: 0.0308 - dense_1_loss_8: 0.0278 - dense_1_loss_9: 0.0272 - dense_1_loss_10: 0.0231 - dense_1_loss_11: 0.0254 - dense_1_loss_12: 0.0216 - dense_1_loss_13: 0.0194 - dense_1_loss_14: 0.0201 - dense_1_loss_15: 0.0207 - dense_1_loss_16: 0.0213 - dense_1_loss_17: 0.0199 - dense_1_loss_18: 0.0207 - dense_1_loss_19: 0.0209 - dense_1_loss_20: 0.0222 - dense_1_loss_21: 0.0220 - dense_1_loss_22: 0.0210 - dense_1_loss_23: 0.0196 - dense_1_loss_24: 0.0211 - dense_1_loss_25: 0.0218 - dense_1_loss_26: 0.0230 - dense_1_loss_27: 0.0235 - dense_1_loss_28: 0.0234 - dense_1_loss_29: 0.0263 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 96/100
60/60 [==============================] - 0s - loss: 5.8412 - dense_1_loss_1: 3.7437 - dense_1_loss_2: 1.1198 - dense_1_loss_3: 0.2988 - dense_1_loss_4: 0.0832 - dense_1_loss_5: 0.0488 - dense_1_loss_6: 0.0344 - dense_1_loss_7: 0.0302 - dense_1_loss_8: 0.0273 - dense_1_loss_9: 0.0267 - dense_1_loss_10: 0.0227 - dense_1_loss_11: 0.0249 - dense_1_loss_12: 0.0212 - dense_1_loss_13: 0.0190 - dense_1_loss_14: 0.0197 - dense_1_loss_15: 0.0203 - dense_1_loss_16: 0.0208 - dense_1_loss_17: 0.0195 - dense_1_loss_18: 0.0203 - dense_1_loss_19: 0.0204 - dense_1_loss_20: 0.0217 - dense_1_loss_21: 0.0216 - dense_1_loss_22: 0.0206 - dense_1_loss_23: 0.0193 - dense_1_loss_24: 0.0206 - dense_1_loss_25: 0.0213 - dense_1_loss_26: 0.0225 - dense_1_loss_27: 0.0230 - dense_1_loss_28: 0.0229 - dense_1_loss_29: 0.0258 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 97/100
60/60 [==============================] - 0s - loss: 5.8096 - dense_1_loss_1: 3.7406 - dense_1_loss_2: 1.1099 - dense_1_loss_3: 0.2939 - dense_1_loss_4: 0.0817 - dense_1_loss_5: 0.0478 - dense_1_loss_6: 0.0337 - dense_1_loss_7: 0.0296 - dense_1_loss_8: 0.0267 - dense_1_loss_9: 0.0262 - dense_1_loss_10: 0.0222 - dense_1_loss_11: 0.0244 - dense_1_loss_12: 0.0208 - dense_1_loss_13: 0.0186 - dense_1_loss_14: 0.0193 - dense_1_loss_15: 0.0199 - dense_1_loss_16: 0.0204 - dense_1_loss_17: 0.0191 - dense_1_loss_18: 0.0199 - dense_1_loss_19: 0.0200 - dense_1_loss_20: 0.0213 - dense_1_loss_21: 0.0212 - dense_1_loss_22: 0.0202 - dense_1_loss_23: 0.0189 - dense_1_loss_24: 0.0202 - dense_1_loss_25: 0.0209 - dense_1_loss_26: 0.0221 - dense_1_loss_27: 0.0226 - dense_1_loss_28: 0.0224 - dense_1_loss_29: 0.0253 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 98/100
60/60 [==============================] - 0s - loss: 5.7808 - dense_1_loss_1: 3.7374 - dense_1_loss_2: 1.1009 - dense_1_loss_3: 0.2898 - dense_1_loss_4: 0.0802 - dense_1_loss_5: 0.0469 - dense_1_loss_6: 0.0330 - dense_1_loss_7: 0.0290 - dense_1_loss_8: 0.0262 - dense_1_loss_9: 0.0258 - dense_1_loss_10: 0.0218 - dense_1_loss_11: 0.0239 - dense_1_loss_12: 0.0204 - dense_1_loss_13: 0.0183 - dense_1_loss_14: 0.0189 - dense_1_loss_15: 0.0195 - dense_1_loss_16: 0.0200 - dense_1_loss_17: 0.0187 - dense_1_loss_18: 0.0195 - dense_1_loss_19: 0.0196 - dense_1_loss_20: 0.0209 - dense_1_loss_21: 0.0208 - dense_1_loss_22: 0.0198 - dense_1_loss_23: 0.0185 - dense_1_loss_24: 0.0198 - dense_1_loss_25: 0.0205 - dense_1_loss_26: 0.0217 - dense_1_loss_27: 0.0221 - dense_1_loss_28: 0.0219 - dense_1_loss_29: 0.0249 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 99/100
60/60 [==============================] - 0s - loss: 5.7520 - dense_1_loss_1: 3.7342 - dense_1_loss_2: 1.0915 - dense_1_loss_3: 0.2857 - dense_1_loss_4: 0.0789 - dense_1_loss_5: 0.0461 - dense_1_loss_6: 0.0324 - dense_1_loss_7: 0.0285 - dense_1_loss_8: 0.0257 - dense_1_loss_9: 0.0253 - dense_1_loss_10: 0.0214 - dense_1_loss_11: 0.0234 - dense_1_loss_12: 0.0200 - dense_1_loss_13: 0.0179 - dense_1_loss_14: 0.0185 - dense_1_loss_15: 0.0191 - dense_1_loss_16: 0.0196 - dense_1_loss_17: 0.0183 - dense_1_loss_18: 0.0191 - dense_1_loss_19: 0.0193 - dense_1_loss_20: 0.0205 - dense_1_loss_21: 0.0204 - dense_1_loss_22: 0.0194 - dense_1_loss_23: 0.0181 - dense_1_loss_24: 0.0194 - dense_1_loss_25: 0.0201 - dense_1_loss_26: 0.0213 - dense_1_loss_27: 0.0216 - dense_1_loss_28: 0.0215 - dense_1_loss_29: 0.0245 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9333 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     
Epoch 100/100
60/60 [==============================] - 0s - loss: 5.7237 - dense_1_loss_1: 3.7312 - dense_1_loss_2: 1.0825 - dense_1_loss_3: 0.2813 - dense_1_loss_4: 0.0776 - dense_1_loss_5: 0.0452 - dense_1_loss_6: 0.0317 - dense_1_loss_7: 0.0280 - dense_1_loss_8: 0.0252 - dense_1_loss_9: 0.0248 - dense_1_loss_10: 0.0211 - dense_1_loss_11: 0.0230 - dense_1_loss_12: 0.0196 - dense_1_loss_13: 0.0176 - dense_1_loss_14: 0.0182 - dense_1_loss_15: 0.0188 - dense_1_loss_16: 0.0192 - dense_1_loss_17: 0.0180 - dense_1_loss_18: 0.0188 - dense_1_loss_19: 0.0189 - dense_1_loss_20: 0.0201 - dense_1_loss_21: 0.0200 - dense_1_loss_22: 0.0190 - dense_1_loss_23: 0.0178 - dense_1_loss_24: 0.0191 - dense_1_loss_25: 0.0197 - dense_1_loss_26: 0.0209 - dense_1_loss_27: 0.0212 - dense_1_loss_28: 0.0211 - dense_1_loss_29: 0.0241 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.6667 - dense_1_acc_3: 0.9500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - dense_1_acc_6: 1.0000 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 1.0000 - dense_1_acc_10: 1.0000 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 1.0000 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 1.0000 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 1.0000 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 1.0000 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 1.0000 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 1.0000 - dense_1_acc_29: 1.0000 - dense_1_acc_30: 0.0000e+00     

<keras.callbacks.History at 0x7fb6defba668>

You should see the model loss going down. Now that you have trained a model, lets go on the the final section to implement an inference algorithm, and generate some music!

3 - Generating music

You now have a trained model which has learned the patterns of the jazz soloist. Lets now use this model to synthesize new music.

3.1 - Predicting & Sampling

At each step of sampling, you will take as input the activation a and cell state c from the previous state of the LSTM, forward propagate by one step, and get a new output activation as well as cell state. The new activation a can then be used to generate the output, using densor as before.

To start off the model, we will initialize x0 as well as the LSTM activation and and cell value a0 and c0 to be zeros.

Exercise: Implement the function below to sample a sequence of musical values. Here are some of the key steps you'll need to implement inside the for-loop that generates the $T_y$ output characters:

Step 2.A: Use LSTM_Cell, which inputs the previous step's c and a to generate the current step's c and a.

Step 2.B: Use densor (defined previously) to compute a softmax on a to get the output for the current step.

Step 2.C: Save the output you have just generated by appending it to outputs.

Step 2.D: Sample x to the be "out"'s one-hot version (the prediction) so that you can pass it to the next LSTM's step. We have already provided this line of code, which uses a Lambda function.

x = Lambda(one_hot)(out)

[Minor technical note: Rather than sampling a value at random according to the probabilities in out, this line of code actually chooses the single most likely note at each step using an argmax.]

# GRADED FUNCTION: music_inference_model

def music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 100):
    """
    Uses the trained "LSTM_cell" and "densor" from model() to generate a sequence of values.
    
    Arguments:
    LSTM_cell -- the trained "LSTM_cell" from model(), Keras layer object
    densor -- the trained "densor" from model(), Keras layer object
    n_values -- integer, umber of unique values
    n_a -- number of units in the LSTM_cell
    Ty -- integer, number of time steps to generate
    
    Returns:
    inference_model -- Keras model instance
    """
    
    # Define the input of your model with a shape 
    x0 = Input(shape=(1, n_values))
    
    # Define s0, initial hidden state for the decoder LSTM
    a0 = Input(shape=(n_a,), name='a0')
    c0 = Input(shape=(n_a,), name='c0')
    a = a0
    c = c0
    x = x0

    ### START CODE HERE ###
    # Step 1: Create an empty list of "outputs" to later store your predicted values (≈1 line)
    outputs = []
    
    # Step 2: Loop over Ty and generate a value at every time step
    for t in range(Ty):
        
        # Step 2.A: Perform one step of LSTM_cell (≈1 line)
        a, _, c = LSTM_cell(x, initial_state=[a, c])
        
        # Step 2.B: Apply Dense layer to the hidden state output of the LSTM_cell (≈1 line)
        out = densor(a)

        # Step 2.C: Append the prediction "out" to "outputs". out.shape = (None, 78) (≈1 line)
        outputs.append(out)
        
        # Step 2.D: Select the next value according to "out", and set "x" to be the one-hot representation of the
        #           selected value, which will be passed as the input to LSTM_cell on the next step. We have provided 
        #           the line of code you need to do this. 
        x = Lambda(one_hot)(out)
        
    # Step 3: Create model instance with the correct "inputs" and "outputs" (≈1 line)
    inference_model = Model([x0, a0, c0], outputs)
    
    ### END CODE HERE ###
    
    return inference_model

Run the cell below to define your inference model. This model is hard coded to generate 50 values.

inference_model = music_inference_model(LSTM_cell, densor, n_values = 78, n_a = 64, Ty = 50)

Finally, this creates the zero-valued vectors you will use to initialize x and the LSTM state variables a and c.

x_initializer = np.zeros((1, 1, 78))
a_initializer = np.zeros((1, n_a))
c_initializer = np.zeros((1, n_a))

Exercise: Implement predict_and_sample(). This function takes many arguments including the inputs [x_initializer, a_initializer, c_initializer]. In order to predict the output corresponding to this input, you will need to carry-out 3 steps:

1. Use your inference model to predict an output given your set of inputs. The output pred should be a list of length 20 where each element is a numpy-array of shape ($T_y$, n_values)
2. Convert pred into a numpy array of $T_y$ indices. Each index corresponds is computed by taking the argmax of an element of the pred list. Hint.
3. Convert the indices into their one-hot vector representations. Hint.

# GRADED FUNCTION: predict_and_sample

def predict_and_sample(inference_model, x_initializer = x_initializer, a_initializer = a_initializer, 
                       c_initializer = c_initializer):
    """
    Predicts the next value of values using the inference model.
    
    Arguments:
    inference_model -- Keras model instance for inference time
    x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation
    a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell
    c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel
    
    Returns:
    results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated
    indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated
    """
    
    ### START CODE HERE ###
    # Step 1: Use your inference model to predict an output sequence given x_initializer, a_initializer and c_initializer.
    pred = inference_model.predict([x_initializer, a_initializer, c_initializer])
    # Step 2: Convert "pred" into an np.array() of indices with the maximum probabilities
    indices = np.argmax(pred, 2)
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices, num_classes=None)
    ### END CODE HERE ###
    
    return results, indices

results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
print("np.argmax(results[12]) =", np.argmax(results[12]))
print("np.argmax(results[17]) =", np.argmax(results[17]))
print("list(indices[12:18]) =", list(indices[12:18]))

np.argmax(results[12]) = 15
np.argmax(results[17]) = 36
list(indices[12:18]) = [array([15]), array([22]), array([31]), array([11]), array([51]), array([36])]

Expected Output: Your results may differ because Keras' results are not completely predictable. However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above, you should very likely observe a sequence of indices that are not all identical. Moreover, you should observe that: np.argmax(results[12]) is the first element of list(indices[12:18]) and np.argmax(results[17]) is the last element of list(indices[12:18]).

**np.argmax(results[12])** =	1
**np.argmax(results[12])** =	42
**list(indices[12:18])** =	[array([1]), array([42]), array([54]), array([17]), array([1]), array([42])]

3.3 - Generate music

Finally, you are ready to generate music. Your RNN generates a sequence of values. The following code generates music by first calling your predict_and_sample() function. These values are then post-processed into musical chords (meaning that multiple values or notes can be played at the same time).

Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. The post-processing does things such as clean up the generated audio by making sure the same sound is not repeated too many times, that two successive notes are not too far from each other in pitch, and so on. One could argue that a lot of these post-processing steps are hacks; also, a lot the music generation literature has also focused on hand-crafting post-processors, and a lot of the output quality depends on the quality of the post-processing and not just the quality of the RNN. But this post-processing does make a huge difference, so lets use it in our implementation as well.

Lets make some music!

Run the following cell to generate music and record it into your out_stream. This can take a couple of minutes.

out_stream = generate_music(inference_model)

Predicting new values for different set of chords.
Generated 51 sounds using the predicted values for the set of chords ("1") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("2") and after pruning
Generated 51 sounds using the predicted values for the set of chords ("3") and after pruning
Generated 50 sounds using the predicted values for the set of chords ("4") and after pruning

To listen to your music, click File->Open... Then go to "output/" and download "my_music.midi". Either play it on your computer with an application that can read midi files if you have one, or use one of the free online "MIDI to mp3" conversion tools to convert this to mp3.

As reference, here also is a 30sec audio clip we generated using this algorithm.

IPython.display.Audio('./data/30s_trained_model.mp3')

Congratulations!

You have come to the end of the notebook.

Here's what you should remember:

• A sequence model can be used to generate musical values, which are then post-processed into midi music.
• Fairly similar models can be used to generate dinosaur names or to generate music, with the major difference being the input fed to the model.
• In Keras, sequence generation involves defining layers with shared weights, which are then repeated for the different time steps $1, \ldots, T_x$.

Congratulations on completing this assignment and generating a jazz solo!

References

The ideas presented in this notebook came primarily from three computational music papers cited below. The implementation here also took significant inspiration and used many components from Ji-Sung Kim's github repository.

• Ji-Sung Kim, 2016, deepjazz
• Jon Gillick, Kevin Tang and Robert Keller, 2009. Learning Jazz Grammars
• Robert Keller and David Morrison, 2007, A Grammatical Approach to Automatic Improvisation
• François Pachet, 1999, Surprising Harmonies

We're also grateful to François Germain for valuable feedback.