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.



In [1]:

    
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:



In [2]:

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









    Out[2]:

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.



In [3]:

    
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.



In [4]:

    
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:

Define the layer objects (we will use global variables for this).
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().



In [5]:

    
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:

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



In [6]:

    
# 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(inputs=[X, a0, c0], outputs=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.



In [7]:

    
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.



In [8]:

    
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.



In [9]:

    
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.



In [10]:

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









    



Epoch 1/100
60/60 [==============================] - 7s - loss: 125.8893 - dense_1_loss_1: 4.3546 - dense_1_loss_2: 4.3497 - dense_1_loss_3: 4.3475 - dense_1_loss_4: 4.3470 - dense_1_loss_5: 4.3450 - dense_1_loss_6: 4.3429 - dense_1_loss_7: 4.3421 - dense_1_loss_8: 4.3409 - dense_1_loss_9: 4.3390 - dense_1_loss_10: 4.3404 - dense_1_loss_11: 4.3374 - dense_1_loss_12: 4.3468 - dense_1_loss_13: 4.3361 - dense_1_loss_14: 4.3377 - dense_1_loss_15: 4.3411 - dense_1_loss_16: 4.3358 - dense_1_loss_17: 4.3444 - dense_1_loss_18: 4.3396 - dense_1_loss_19: 4.3410 - dense_1_loss_20: 4.3447 - dense_1_loss_21: 4.3357 - dense_1_loss_22: 4.3326 - dense_1_loss_23: 4.3336 - dense_1_loss_24: 4.3325 - dense_1_loss_25: 4.3404 - dense_1_loss_26: 4.3357 - dense_1_loss_27: 4.3413 - dense_1_loss_28: 4.3363 - dense_1_loss_29: 4.3475 - 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: 0.0667 - dense_1_acc_4: 0.0333 - dense_1_acc_5: 0.0500 - dense_1_acc_6: 0.0667 - dense_1_acc_7: 0.0167 - dense_1_acc_8: 0.0333 - dense_1_acc_9: 0.0500 - dense_1_acc_10: 0.0500 - dense_1_acc_11: 0.0833 - dense_1_acc_12: 0.0167 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.0833 - dense_1_acc_15: 0.0667 - dense_1_acc_16: 0.0667 - dense_1_acc_17: 0.0667 - dense_1_acc_18: 0.0000e+00 - dense_1_acc_19: 0.0333 - dense_1_acc_20: 0.0500 - dense_1_acc_21: 0.0667 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.0167 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.0167 - dense_1_acc_29: 0.0500 - dense_1_acc_30: 0.0000e+00                                                                                                         
Epoch 2/100
60/60 [==============================] - 0s - loss: 122.0632 - dense_1_loss_1: 4.3320 - dense_1_loss_2: 4.3025 - dense_1_loss_3: 4.2771 - dense_1_loss_4: 4.2838 - dense_1_loss_5: 4.2633 - dense_1_loss_6: 4.2565 - dense_1_loss_7: 4.2431 - dense_1_loss_8: 4.2240 - dense_1_loss_9: 4.2349 - dense_1_loss_10: 4.2024 - dense_1_loss_11: 4.1843 - dense_1_loss_12: 4.2402 - dense_1_loss_13: 4.1897 - dense_1_loss_14: 4.1943 - dense_1_loss_15: 4.1778 - dense_1_loss_16: 4.1736 - dense_1_loss_17: 4.2029 - dense_1_loss_18: 4.2013 - dense_1_loss_19: 4.1667 - dense_1_loss_20: 4.2151 - dense_1_loss_21: 4.1865 - dense_1_loss_22: 4.1321 - dense_1_loss_23: 4.1692 - dense_1_loss_24: 4.1640 - dense_1_loss_25: 4.1918 - dense_1_loss_26: 4.1368 - dense_1_loss_27: 4.1320 - dense_1_loss_28: 4.1721 - dense_1_loss_29: 4.2132 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.1333 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1833 - dense_1_acc_8: 0.1500 - dense_1_acc_9: 0.1167 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.1667 - dense_1_acc_13: 0.1667 - dense_1_acc_14: 0.1000 - dense_1_acc_15: 0.1333 - dense_1_acc_16: 0.1333 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0500 - dense_1_acc_21: 0.1500 - dense_1_acc_22: 0.1667 - dense_1_acc_23: 0.1000 - dense_1_acc_24: 0.1667 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.1167 - dense_1_acc_27: 0.1333 - dense_1_acc_28: 0.0833 - dense_1_acc_29: 0.0833 - dense_1_acc_30: 0.0000e+00     
Epoch 3/100
60/60 [==============================] - 0s - loss: 116.6106 - dense_1_loss_1: 4.3099 - dense_1_loss_2: 4.2510 - dense_1_loss_3: 4.1905 - dense_1_loss_4: 4.1851 - dense_1_loss_5: 4.1455 - dense_1_loss_6: 4.1385 - dense_1_loss_7: 4.0876 - dense_1_loss_8: 4.0155 - dense_1_loss_9: 4.0022 - dense_1_loss_10: 3.8709 - dense_1_loss_11: 3.8313 - dense_1_loss_12: 4.0908 - dense_1_loss_13: 3.9004 - dense_1_loss_14: 3.8914 - dense_1_loss_15: 3.9417 - dense_1_loss_16: 3.9167 - dense_1_loss_17: 4.0582 - dense_1_loss_18: 4.0898 - dense_1_loss_19: 3.8372 - dense_1_loss_20: 4.0859 - dense_1_loss_21: 4.0833 - dense_1_loss_22: 3.8613 - dense_1_loss_23: 3.9252 - dense_1_loss_24: 3.8540 - dense_1_loss_25: 4.0987 - dense_1_loss_26: 3.8046 - dense_1_loss_27: 3.8657 - dense_1_loss_28: 4.0210 - dense_1_loss_29: 4.2566 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.0667 - dense_1_acc_2: 0.1833 - dense_1_acc_3: 0.2333 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.1833 - dense_1_acc_6: 0.0833 - dense_1_acc_7: 0.1667 - dense_1_acc_8: 0.1167 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1500 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.0833 - dense_1_acc_14: 0.1333 - dense_1_acc_15: 0.1333 - dense_1_acc_16: 0.1000 - dense_1_acc_17: 0.0833 - dense_1_acc_18: 0.0667 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.0333 - dense_1_acc_21: 0.0167 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.0500 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.0667 - dense_1_acc_26: 0.0833 - dense_1_acc_27: 0.0500 - dense_1_acc_28: 0.1000 - dense_1_acc_29: 0.0333 - dense_1_acc_30: 0.0000e+00                 
Epoch 4/100
60/60 [==============================] - 0s - loss: 112.4659 - dense_1_loss_1: 4.2892 - dense_1_loss_2: 4.2099 - dense_1_loss_3: 4.1100 - dense_1_loss_4: 4.0991 - dense_1_loss_5: 4.0141 - dense_1_loss_6: 4.0140 - dense_1_loss_7: 3.9539 - dense_1_loss_8: 3.7644 - dense_1_loss_9: 3.8387 - dense_1_loss_10: 3.6774 - dense_1_loss_11: 3.7420 - dense_1_loss_12: 4.0561 - dense_1_loss_13: 3.7598 - dense_1_loss_14: 3.7139 - dense_1_loss_15: 3.7739 - dense_1_loss_16: 3.7435 - dense_1_loss_17: 3.8510 - dense_1_loss_18: 3.8699 - dense_1_loss_19: 3.6853 - dense_1_loss_20: 3.9388 - dense_1_loss_21: 3.9113 - dense_1_loss_22: 3.8413 - dense_1_loss_23: 3.8228 - dense_1_loss_24: 3.7260 - dense_1_loss_25: 3.9282 - dense_1_loss_26: 3.5712 - dense_1_loss_27: 3.6760 - dense_1_loss_28: 3.8690 - dense_1_loss_29: 4.0151 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1667 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2167 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.1000 - dense_1_acc_7: 0.1000 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2333 - dense_1_acc_10: 0.1167 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1167 - dense_1_acc_14: 0.0833 - dense_1_acc_15: 0.0833 - dense_1_acc_16: 0.1333 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.0667 - dense_1_acc_19: 0.0667 - dense_1_acc_20: 0.1000 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.0333 - dense_1_acc_23: 0.0500 - dense_1_acc_24: 0.0667 - dense_1_acc_25: 0.0500 - dense_1_acc_26: 0.1500 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.0500 - dense_1_acc_29: 0.0667 - dense_1_acc_30: 0.0000e+00         
Epoch 5/100
60/60 [==============================] - 0s - loss: 110.3376 - dense_1_loss_1: 4.2736 - dense_1_loss_2: 4.1736 - dense_1_loss_3: 4.0446 - dense_1_loss_4: 4.0232 - dense_1_loss_5: 3.9116 - dense_1_loss_6: 3.9255 - dense_1_loss_7: 3.9113 - dense_1_loss_8: 3.6618 - dense_1_loss_9: 3.7535 - dense_1_loss_10: 3.5999 - dense_1_loss_11: 3.6612 - dense_1_loss_12: 3.9566 - dense_1_loss_13: 3.6950 - dense_1_loss_14: 3.5782 - dense_1_loss_15: 3.7166 - dense_1_loss_16: 3.6914 - dense_1_loss_17: 3.8181 - dense_1_loss_18: 3.7692 - dense_1_loss_19: 3.6283 - dense_1_loss_20: 3.8401 - dense_1_loss_21: 3.8714 - dense_1_loss_22: 3.7511 - dense_1_loss_23: 3.7248 - dense_1_loss_24: 3.6421 - dense_1_loss_25: 3.8957 - dense_1_loss_26: 3.5521 - dense_1_loss_27: 3.5994 - dense_1_loss_28: 3.7649 - dense_1_loss_29: 3.9028 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1333 - dense_1_acc_2: 0.0833 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.1667 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1333 - dense_1_acc_7: 0.1167 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.1333 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1167 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1000 - dense_1_acc_14: 0.0833 - dense_1_acc_15: 0.1000 - dense_1_acc_16: 0.1000 - dense_1_acc_17: 0.1500 - dense_1_acc_18: 0.0833 - dense_1_acc_19: 0.0833 - dense_1_acc_20: 0.1333 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.0667 - dense_1_acc_23: 0.0667 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.1667 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.1167 - dense_1_acc_29: 0.1667 - dense_1_acc_30: 0.0000e+00         
Epoch 6/100
60/60 [==============================] - 0s - loss: 107.3910 - dense_1_loss_1: 4.2593 - dense_1_loss_2: 4.1424 - dense_1_loss_3: 3.9793 - dense_1_loss_4: 3.9415 - dense_1_loss_5: 3.8205 - dense_1_loss_6: 3.8456 - dense_1_loss_7: 3.8459 - dense_1_loss_8: 3.5466 - dense_1_loss_9: 3.6313 - dense_1_loss_10: 3.5009 - dense_1_loss_11: 3.5976 - dense_1_loss_12: 3.8224 - dense_1_loss_13: 3.6205 - dense_1_loss_14: 3.4863 - dense_1_loss_15: 3.6204 - dense_1_loss_16: 3.5930 - dense_1_loss_17: 3.6184 - dense_1_loss_18: 3.6555 - dense_1_loss_19: 3.4933 - dense_1_loss_20: 3.7586 - dense_1_loss_21: 3.7014 - dense_1_loss_22: 3.5975 - dense_1_loss_23: 3.6435 - dense_1_loss_24: 3.5805 - dense_1_loss_25: 3.8105 - dense_1_loss_26: 3.4195 - dense_1_loss_27: 3.5333 - dense_1_loss_28: 3.6205 - dense_1_loss_29: 3.7048 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.1833 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.1500 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.1500 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1167 - dense_1_acc_16: 0.1333 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.1000 - dense_1_acc_19: 0.1333 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.1000 - dense_1_acc_22: 0.1000 - dense_1_acc_23: 0.0333 - dense_1_acc_24: 0.0500 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.1500 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.1500 - dense_1_acc_29: 0.1333 - dense_1_acc_30: 0.0000e+00     
Epoch 7/100
60/60 [==============================] - 0s - loss: 103.6675 - dense_1_loss_1: 4.2447 - dense_1_loss_2: 4.1121 - dense_1_loss_3: 3.9106 - dense_1_loss_4: 3.8606 - dense_1_loss_5: 3.7157 - dense_1_loss_6: 3.7736 - dense_1_loss_7: 3.7525 - dense_1_loss_8: 3.4131 - dense_1_loss_9: 3.4991 - dense_1_loss_10: 3.3570 - dense_1_loss_11: 3.4470 - dense_1_loss_12: 3.6495 - dense_1_loss_13: 3.4545 - dense_1_loss_14: 3.3671 - dense_1_loss_15: 3.4354 - dense_1_loss_16: 3.4480 - dense_1_loss_17: 3.4419 - dense_1_loss_18: 3.5193 - dense_1_loss_19: 3.2935 - dense_1_loss_20: 3.5984 - dense_1_loss_21: 3.5931 - dense_1_loss_22: 3.4500 - dense_1_loss_23: 3.4505 - dense_1_loss_24: 3.4253 - dense_1_loss_25: 3.7168 - dense_1_loss_26: 3.2747 - dense_1_loss_27: 3.3982 - dense_1_loss_28: 3.4816 - dense_1_loss_29: 3.5838 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2333 - dense_1_acc_6: 0.1500 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.2167 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1000 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1667 - dense_1_acc_15: 0.1500 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.0833 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.0833 - dense_1_acc_28: 0.1833 - dense_1_acc_29: 0.1500 - dense_1_acc_30: 0.0000e+00     
Epoch 8/100
60/60 [==============================] - 0s - loss: 100.2212 - dense_1_loss_1: 4.2323 - dense_1_loss_2: 4.0739 - dense_1_loss_3: 3.8467 - dense_1_loss_4: 3.7766 - dense_1_loss_5: 3.6080 - dense_1_loss_6: 3.6802 - dense_1_loss_7: 3.6436 - dense_1_loss_8: 3.2689 - dense_1_loss_9: 3.3296 - dense_1_loss_10: 3.1911 - dense_1_loss_11: 3.3272 - dense_1_loss_12: 3.4890 - dense_1_loss_13: 3.2759 - dense_1_loss_14: 3.1894 - dense_1_loss_15: 3.2862 - dense_1_loss_16: 3.3325 - dense_1_loss_17: 3.3166 - dense_1_loss_18: 3.3888 - dense_1_loss_19: 3.1777 - dense_1_loss_20: 3.4860 - dense_1_loss_21: 3.4691 - dense_1_loss_22: 3.2904 - dense_1_loss_23: 3.3682 - dense_1_loss_24: 3.3718 - dense_1_loss_25: 3.5720 - dense_1_loss_26: 3.1471 - dense_1_loss_27: 3.3230 - dense_1_loss_28: 3.3196 - dense_1_loss_29: 3.4399 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2167 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2000 - dense_1_acc_6: 0.1167 - dense_1_acc_7: 0.1333 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.1333 - dense_1_acc_11: 0.1500 - dense_1_acc_12: 0.1167 - dense_1_acc_13: 0.1333 - dense_1_acc_14: 0.1833 - dense_1_acc_15: 0.1667 - dense_1_acc_16: 0.1500 - dense_1_acc_17: 0.1667 - dense_1_acc_18: 0.1167 - dense_1_acc_19: 0.1500 - dense_1_acc_20: 0.1167 - dense_1_acc_21: 0.0833 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1167 - dense_1_acc_24: 0.0833 - dense_1_acc_25: 0.0833 - dense_1_acc_26: 0.2167 - dense_1_acc_27: 0.1000 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.1167 - dense_1_acc_30: 0.0000e+00     
Epoch 9/100
60/60 [==============================] - 0s - loss: 96.1836 - dense_1_loss_1: 4.2205 - dense_1_loss_2: 4.0382 - dense_1_loss_3: 3.7733 - dense_1_loss_4: 3.7017 - dense_1_loss_5: 3.4962 - dense_1_loss_6: 3.5828 - dense_1_loss_7: 3.5138 - dense_1_loss_8: 3.1359 - dense_1_loss_9: 3.1748 - dense_1_loss_10: 3.0417 - dense_1_loss_11: 3.2007 - dense_1_loss_12: 3.3364 - dense_1_loss_13: 3.0825 - dense_1_loss_14: 3.0666 - dense_1_loss_15: 3.0929 - dense_1_loss_16: 3.2007 - dense_1_loss_17: 3.1213 - dense_1_loss_18: 3.2603 - dense_1_loss_19: 3.0104 - dense_1_loss_20: 3.3016 - dense_1_loss_21: 3.2879 - dense_1_loss_22: 3.2062 - dense_1_loss_23: 3.2021 - dense_1_loss_24: 3.1936 - dense_1_loss_25: 3.3863 - dense_1_loss_26: 2.9837 - dense_1_loss_27: 3.1287 - dense_1_loss_28: 3.1950 - dense_1_loss_29: 3.2476 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1167 - dense_1_acc_3: 0.2333 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2167 - dense_1_acc_6: 0.1667 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.2500 - dense_1_acc_9: 0.2167 - dense_1_acc_10: 0.1667 - dense_1_acc_11: 0.1667 - dense_1_acc_12: 0.1333 - dense_1_acc_13: 0.2000 - dense_1_acc_14: 0.1833 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.1500 - dense_1_acc_19: 0.1833 - dense_1_acc_20: 0.1500 - dense_1_acc_21: 0.1167 - dense_1_acc_22: 0.1333 - dense_1_acc_23: 0.1333 - dense_1_acc_24: 0.1000 - dense_1_acc_25: 0.1167 - dense_1_acc_26: 0.2333 - dense_1_acc_27: 0.1667 - dense_1_acc_28: 0.2000 - dense_1_acc_29: 0.1667 - dense_1_acc_30: 0.0000e+00     
Epoch 10/100
60/60 [==============================] - 0s - loss: 92.3168 - dense_1_loss_1: 4.2109 - dense_1_loss_2: 4.0016 - dense_1_loss_3: 3.7035 - dense_1_loss_4: 3.6242 - dense_1_loss_5: 3.3899 - dense_1_loss_6: 3.4586 - dense_1_loss_7: 3.3783 - dense_1_loss_8: 3.0273 - dense_1_loss_9: 3.0331 - dense_1_loss_10: 2.9083 - dense_1_loss_11: 3.0726 - dense_1_loss_12: 3.1883 - dense_1_loss_13: 2.9210 - dense_1_loss_14: 2.9194 - dense_1_loss_15: 2.9622 - dense_1_loss_16: 3.0952 - dense_1_loss_17: 2.9987 - dense_1_loss_18: 3.1123 - dense_1_loss_19: 2.8533 - dense_1_loss_20: 3.1318 - dense_1_loss_21: 3.0772 - dense_1_loss_22: 3.0491 - dense_1_loss_23: 3.0215 - dense_1_loss_24: 3.0474 - dense_1_loss_25: 3.2242 - dense_1_loss_26: 2.7951 - dense_1_loss_27: 2.9711 - dense_1_loss_28: 3.0115 - dense_1_loss_29: 3.1289 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1333 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2500 - dense_1_acc_6: 0.2000 - dense_1_acc_7: 0.1500 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.1833 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.3167 - dense_1_acc_15: 0.3167 - dense_1_acc_16: 0.2500 - dense_1_acc_17: 0.2667 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2500 - dense_1_acc_20: 0.2000 - dense_1_acc_21: 0.1833 - dense_1_acc_22: 0.2167 - dense_1_acc_23: 0.2333 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1500 - dense_1_acc_26: 0.2833 - dense_1_acc_27: 0.2000 - dense_1_acc_28: 0.2333 - dense_1_acc_29: 0.1833 - dense_1_acc_30: 0.0000e+00     
Epoch 11/100
60/60 [==============================] - 0s - loss: 90.3161 - dense_1_loss_1: 4.2010 - dense_1_loss_2: 3.9677 - dense_1_loss_3: 3.6376 - dense_1_loss_4: 3.5485 - dense_1_loss_5: 3.3040 - dense_1_loss_6: 3.3417 - dense_1_loss_7: 3.2588 - dense_1_loss_8: 2.9271 - dense_1_loss_9: 2.9229 - dense_1_loss_10: 2.7676 - dense_1_loss_11: 2.9765 - dense_1_loss_12: 3.1424 - dense_1_loss_13: 2.8288 - dense_1_loss_14: 2.8663 - dense_1_loss_15: 2.8381 - dense_1_loss_16: 3.0525 - dense_1_loss_17: 2.9352 - dense_1_loss_18: 3.0444 - dense_1_loss_19: 2.7881 - dense_1_loss_20: 3.0496 - dense_1_loss_21: 3.0463 - dense_1_loss_22: 2.9356 - dense_1_loss_23: 2.9310 - dense_1_loss_24: 3.0042 - dense_1_loss_25: 3.1710 - dense_1_loss_26: 2.7544 - dense_1_loss_27: 2.9278 - dense_1_loss_28: 3.0426 - dense_1_loss_29: 3.1046 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.2333 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.2000 - dense_1_acc_7: 0.2000 - dense_1_acc_8: 0.2500 - dense_1_acc_9: 0.2667 - dense_1_acc_10: 0.2500 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.1500 - dense_1_acc_13: 0.3333 - dense_1_acc_14: 0.3167 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.1500 - dense_1_acc_19: 0.2333 - dense_1_acc_20: 0.1667 - dense_1_acc_21: 0.1333 - dense_1_acc_22: 0.1167 - dense_1_acc_23: 0.2167 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.2667 - dense_1_acc_27: 0.1833 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.1167 - dense_1_acc_30: 0.0167         
Epoch 12/100
60/60 [==============================] - 0s - loss: 85.9815 - dense_1_loss_1: 4.1922 - dense_1_loss_2: 3.9349 - dense_1_loss_3: 3.5727 - dense_1_loss_4: 3.4658 - dense_1_loss_5: 3.1953 - dense_1_loss_6: 3.1945 - dense_1_loss_7: 3.1019 - dense_1_loss_8: 2.8191 - dense_1_loss_9: 2.7696 - dense_1_loss_10: 2.6549 - dense_1_loss_11: 2.9106 - dense_1_loss_12: 2.9394 - dense_1_loss_13: 2.6462 - dense_1_loss_14: 2.6565 - dense_1_loss_15: 2.7302 - dense_1_loss_16: 2.8821 - dense_1_loss_17: 2.7940 - dense_1_loss_18: 2.8194 - dense_1_loss_19: 2.6512 - dense_1_loss_20: 2.7970 - dense_1_loss_21: 2.7866 - dense_1_loss_22: 2.7576 - dense_1_loss_23: 2.8517 - dense_1_loss_24: 2.8417 - dense_1_loss_25: 2.9476 - dense_1_loss_26: 2.6100 - dense_1_loss_27: 2.7586 - dense_1_loss_28: 2.8657 - dense_1_loss_29: 2.8343 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2667 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.3000 - dense_1_acc_6: 0.2333 - dense_1_acc_7: 0.2167 - dense_1_acc_8: 0.2500 - dense_1_acc_9: 0.2833 - dense_1_acc_10: 0.2833 - dense_1_acc_11: 0.2167 - dense_1_acc_12: 0.1667 - dense_1_acc_13: 0.3000 - dense_1_acc_14: 0.3333 - dense_1_acc_15: 0.2833 - dense_1_acc_16: 0.1667 - dense_1_acc_17: 0.2333 - dense_1_acc_18: 0.2000 - dense_1_acc_19: 0.2167 - dense_1_acc_20: 0.2500 - dense_1_acc_21: 0.2500 - dense_1_acc_22: 0.2667 - dense_1_acc_23: 0.1833 - dense_1_acc_24: 0.2333 - dense_1_acc_25: 0.2000 - dense_1_acc_26: 0.3833 - dense_1_acc_27: 0.2167 - dense_1_acc_28: 0.2167 - dense_1_acc_29: 0.2500 - dense_1_acc_30: 0.0167     
Epoch 13/100
60/60 [==============================] - 0s - loss: 82.3401 - dense_1_loss_1: 4.1837 - dense_1_loss_2: 3.9037 - dense_1_loss_3: 3.5097 - dense_1_loss_4: 3.3809 - dense_1_loss_5: 3.1034 - dense_1_loss_6: 3.0706 - dense_1_loss_7: 2.9735 - dense_1_loss_8: 2.7244 - dense_1_loss_9: 2.7051 - dense_1_loss_10: 2.5276 - dense_1_loss_11: 2.8000 - dense_1_loss_12: 2.8012 - dense_1_loss_13: 2.5230 - dense_1_loss_14: 2.5386 - dense_1_loss_15: 2.6794 - dense_1_loss_16: 2.7829 - dense_1_loss_17: 2.6758 - dense_1_loss_18: 2.6696 - dense_1_loss_19: 2.4978 - dense_1_loss_20: 2.5947 - dense_1_loss_21: 2.6352 - dense_1_loss_22: 2.5310 - dense_1_loss_23: 2.7109 - dense_1_loss_24: 2.6065 - dense_1_loss_25: 2.8300 - dense_1_loss_26: 2.4440 - dense_1_loss_27: 2.5713 - dense_1_loss_28: 2.7280 - dense_1_loss_29: 2.6377 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2333 - dense_1_acc_4: 0.2500 - dense_1_acc_5: 0.2667 - dense_1_acc_6: 0.2333 - dense_1_acc_7: 0.2000 - dense_1_acc_8: 0.2333 - dense_1_acc_9: 0.2833 - dense_1_acc_10: 0.3333 - dense_1_acc_11: 0.2333 - dense_1_acc_12: 0.1833 - dense_1_acc_13: 0.2833 - dense_1_acc_14: 0.2333 - dense_1_acc_15: 0.2000 - dense_1_acc_16: 0.2000 - dense_1_acc_17: 0.2000 - dense_1_acc_18: 0.1333 - dense_1_acc_19: 0.2500 - dense_1_acc_20: 0.2667 - dense_1_acc_21: 0.2333 - dense_1_acc_22: 0.3333 - dense_1_acc_23: 0.2000 - dense_1_acc_24: 0.1833 - dense_1_acc_25: 0.1333 - dense_1_acc_26: 0.3167 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.1833 - dense_1_acc_29: 0.2333 - dense_1_acc_30: 0.0000e+00     
Epoch 14/100
60/60 [==============================] - 0s - loss: 79.0230 - dense_1_loss_1: 4.1758 - dense_1_loss_2: 3.8691 - dense_1_loss_3: 3.4380 - dense_1_loss_4: 3.2842 - dense_1_loss_5: 2.9921 - dense_1_loss_6: 2.9425 - dense_1_loss_7: 2.8520 - dense_1_loss_8: 2.6167 - dense_1_loss_9: 2.5519 - dense_1_loss_10: 2.4256 - dense_1_loss_11: 2.6533 - dense_1_loss_12: 2.6501 - dense_1_loss_13: 2.3672 - dense_1_loss_14: 2.4098 - dense_1_loss_15: 2.5511 - dense_1_loss_16: 2.6850 - dense_1_loss_17: 2.5393 - dense_1_loss_18: 2.5661 - dense_1_loss_19: 2.4412 - dense_1_loss_20: 2.4743 - dense_1_loss_21: 2.4947 - dense_1_loss_22: 2.4350 - dense_1_loss_23: 2.6064 - dense_1_loss_24: 2.4813 - dense_1_loss_25: 2.6326 - dense_1_loss_26: 2.3532 - dense_1_loss_27: 2.5058 - dense_1_loss_28: 2.5238 - dense_1_loss_29: 2.5049 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2500 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3167 - dense_1_acc_6: 0.2167 - dense_1_acc_7: 0.2167 - dense_1_acc_8: 0.2500 - dense_1_acc_9: 0.3167 - dense_1_acc_10: 0.3167 - dense_1_acc_11: 0.2667 - dense_1_acc_12: 0.2667 - dense_1_acc_13: 0.3500 - dense_1_acc_14: 0.4000 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.2167 - dense_1_acc_18: 0.1667 - dense_1_acc_19: 0.3333 - dense_1_acc_20: 0.3000 - dense_1_acc_21: 0.2833 - dense_1_acc_22: 0.2333 - dense_1_acc_23: 0.3167 - dense_1_acc_24: 0.2500 - dense_1_acc_25: 0.2167 - dense_1_acc_26: 0.4000 - dense_1_acc_27: 0.3667 - dense_1_acc_28: 0.3167 - dense_1_acc_29: 0.3167 - dense_1_acc_30: 0.0000e+00     
Epoch 15/100
60/60 [==============================] - 0s - loss: 75.3392 - dense_1_loss_1: 4.1673 - dense_1_loss_2: 3.8321 - dense_1_loss_3: 3.3606 - dense_1_loss_4: 3.1825 - dense_1_loss_5: 2.8664 - dense_1_loss_6: 2.8149 - dense_1_loss_7: 2.7226 - dense_1_loss_8: 2.4874 - dense_1_loss_9: 2.4518 - dense_1_loss_10: 2.3297 - dense_1_loss_11: 2.5872 - dense_1_loss_12: 2.4878 - dense_1_loss_13: 2.2307 - dense_1_loss_14: 2.2440 - dense_1_loss_15: 2.4424 - dense_1_loss_16: 2.4938 - dense_1_loss_17: 2.3740 - dense_1_loss_18: 2.3755 - dense_1_loss_19: 2.3681 - dense_1_loss_20: 2.3330 - dense_1_loss_21: 2.3156 - dense_1_loss_22: 2.2738 - dense_1_loss_23: 2.4752 - dense_1_loss_24: 2.3382 - dense_1_loss_25: 2.5172 - dense_1_loss_26: 2.1520 - dense_1_loss_27: 2.3803 - dense_1_loss_28: 2.3634 - dense_1_loss_29: 2.3717 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2667 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.2833 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.3167 - dense_1_acc_9: 0.4167 - dense_1_acc_10: 0.3833 - dense_1_acc_11: 0.2500 - dense_1_acc_12: 0.2667 - dense_1_acc_13: 0.4167 - dense_1_acc_14: 0.4500 - dense_1_acc_15: 0.3000 - dense_1_acc_16: 0.2333 - dense_1_acc_17: 0.3167 - dense_1_acc_18: 0.2667 - dense_1_acc_19: 0.3000 - dense_1_acc_20: 0.3500 - dense_1_acc_21: 0.3333 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.3000 - dense_1_acc_24: 0.2167 - dense_1_acc_25: 0.1500 - dense_1_acc_26: 0.4167 - dense_1_acc_27: 0.3333 - dense_1_acc_28: 0.3000 - dense_1_acc_29: 0.3167 - dense_1_acc_30: 0.0000e+00     
Epoch 16/100
60/60 [==============================] - 0s - loss: 71.8703 - dense_1_loss_1: 4.1579 - dense_1_loss_2: 3.7907 - dense_1_loss_3: 3.2805 - dense_1_loss_4: 3.0752 - dense_1_loss_5: 2.7540 - dense_1_loss_6: 2.7052 - dense_1_loss_7: 2.5986 - dense_1_loss_8: 2.3689 - dense_1_loss_9: 2.3391 - dense_1_loss_10: 2.1970 - dense_1_loss_11: 2.4376 - dense_1_loss_12: 2.3885 - dense_1_loss_13: 2.1083 - dense_1_loss_14: 2.2053 - dense_1_loss_15: 2.3252 - dense_1_loss_16: 2.3721 - dense_1_loss_17: 2.2527 - dense_1_loss_18: 2.2182 - dense_1_loss_19: 2.2729 - dense_1_loss_20: 2.2093 - dense_1_loss_21: 2.2051 - dense_1_loss_22: 2.1330 - dense_1_loss_23: 2.2398 - dense_1_loss_24: 2.1921 - dense_1_loss_25: 2.4045 - dense_1_loss_26: 1.9708 - dense_1_loss_27: 2.2514 - dense_1_loss_28: 2.1963 - dense_1_loss_29: 2.2203 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.1500 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3000 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.3000 - dense_1_acc_9: 0.3500 - dense_1_acc_10: 0.3500 - dense_1_acc_11: 0.2833 - dense_1_acc_12: 0.2500 - dense_1_acc_13: 0.4500 - dense_1_acc_14: 0.4167 - dense_1_acc_15: 0.3167 - dense_1_acc_16: 0.2667 - dense_1_acc_17: 0.3667 - dense_1_acc_18: 0.3333 - dense_1_acc_19: 0.3833 - dense_1_acc_20: 0.4667 - dense_1_acc_21: 0.3333 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.3667 - dense_1_acc_24: 0.3000 - dense_1_acc_25: 0.2500 - dense_1_acc_26: 0.5333 - dense_1_acc_27: 0.3833 - dense_1_acc_28: 0.4000 - dense_1_acc_29: 0.3667 - dense_1_acc_30: 0.0000e+00     
Epoch 17/100
60/60 [==============================] - 0s - loss: 68.4076 - dense_1_loss_1: 4.1496 - dense_1_loss_2: 3.7487 - dense_1_loss_3: 3.2003 - dense_1_loss_4: 2.9690 - dense_1_loss_5: 2.6248 - dense_1_loss_6: 2.5509 - dense_1_loss_7: 2.4317 - dense_1_loss_8: 2.2132 - dense_1_loss_9: 2.2202 - dense_1_loss_10: 2.1221 - dense_1_loss_11: 2.3141 - dense_1_loss_12: 2.1862 - dense_1_loss_13: 1.9517 - dense_1_loss_14: 2.0005 - dense_1_loss_15: 2.2424 - dense_1_loss_16: 2.2110 - dense_1_loss_17: 2.1187 - dense_1_loss_18: 2.0803 - dense_1_loss_19: 2.1601 - dense_1_loss_20: 2.1026 - dense_1_loss_21: 2.0692 - dense_1_loss_22: 2.0304 - dense_1_loss_23: 2.1464 - dense_1_loss_24: 2.0946 - dense_1_loss_25: 2.2564 - dense_1_loss_26: 1.8751 - dense_1_loss_27: 2.1566 - dense_1_loss_28: 2.0636 - dense_1_loss_29: 2.1172 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2000 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3167 - dense_1_acc_7: 0.2667 - dense_1_acc_8: 0.3500 - dense_1_acc_9: 0.4000 - dense_1_acc_10: 0.3833 - dense_1_acc_11: 0.3333 - dense_1_acc_12: 0.3333 - dense_1_acc_13: 0.4667 - dense_1_acc_14: 0.5167 - dense_1_acc_15: 0.2667 - dense_1_acc_16: 0.3000 - dense_1_acc_17: 0.3333 - dense_1_acc_18: 0.3333 - dense_1_acc_19: 0.3667 - dense_1_acc_20: 0.4333 - dense_1_acc_21: 0.3833 - dense_1_acc_22: 0.3667 - dense_1_acc_23: 0.4500 - dense_1_acc_24: 0.3833 - dense_1_acc_25: 0.2667 - dense_1_acc_26: 0.5167 - dense_1_acc_27: 0.4000 - dense_1_acc_28: 0.4500 - dense_1_acc_29: 0.3000 - dense_1_acc_30: 0.0000e+00     
Epoch 18/100
60/60 [==============================] - 0s - loss: 65.1298 - dense_1_loss_1: 4.1409 - dense_1_loss_2: 3.7033 - dense_1_loss_3: 3.1222 - dense_1_loss_4: 2.8623 - dense_1_loss_5: 2.5009 - dense_1_loss_6: 2.4313 - dense_1_loss_7: 2.2911 - dense_1_loss_8: 2.0961 - dense_1_loss_9: 2.1104 - dense_1_loss_10: 2.0389 - dense_1_loss_11: 2.2047 - dense_1_loss_12: 2.0991 - dense_1_loss_13: 1.8938 - dense_1_loss_14: 1.9208 - dense_1_loss_15: 2.0859 - dense_1_loss_16: 2.0932 - dense_1_loss_17: 1.9840 - dense_1_loss_18: 1.9873 - dense_1_loss_19: 2.0204 - dense_1_loss_20: 1.9544 - dense_1_loss_21: 1.9730 - dense_1_loss_22: 1.9527 - dense_1_loss_23: 2.0017 - dense_1_loss_24: 1.9435 - dense_1_loss_25: 2.0867 - dense_1_loss_26: 1.7484 - dense_1_loss_27: 2.0030 - dense_1_loss_28: 1.9553 - dense_1_loss_29: 1.9246 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2167 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2667 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3333 - dense_1_acc_7: 0.3833 - dense_1_acc_8: 0.4000 - dense_1_acc_9: 0.4667 - dense_1_acc_10: 0.4500 - dense_1_acc_11: 0.4000 - dense_1_acc_12: 0.3667 - dense_1_acc_13: 0.5000 - dense_1_acc_14: 0.5167 - dense_1_acc_15: 0.4167 - dense_1_acc_16: 0.4333 - dense_1_acc_17: 0.4167 - dense_1_acc_18: 0.4167 - dense_1_acc_19: 0.4333 - dense_1_acc_20: 0.5333 - dense_1_acc_21: 0.3667 - dense_1_acc_22: 0.4167 - dense_1_acc_23: 0.4500 - dense_1_acc_24: 0.4167 - dense_1_acc_25: 0.3667 - dense_1_acc_26: 0.6167 - dense_1_acc_27: 0.4667 - dense_1_acc_28: 0.4333 - dense_1_acc_29: 0.4667 - dense_1_acc_30: 0.0000e+00     
Epoch 19/100
60/60 [==============================] - 0s - loss: 62.0773 - dense_1_loss_1: 4.1317 - dense_1_loss_2: 3.6584 - dense_1_loss_3: 3.0385 - dense_1_loss_4: 2.7643 - dense_1_loss_5: 2.3836 - dense_1_loss_6: 2.3076 - dense_1_loss_7: 2.1531 - dense_1_loss_8: 1.9636 - dense_1_loss_9: 2.0010 - dense_1_loss_10: 1.8968 - dense_1_loss_11: 2.0591 - dense_1_loss_12: 1.9620 - dense_1_loss_13: 1.7554 - dense_1_loss_14: 1.8154 - dense_1_loss_15: 1.9413 - dense_1_loss_16: 2.0140 - dense_1_loss_17: 1.8646 - dense_1_loss_18: 1.8703 - dense_1_loss_19: 1.9011 - dense_1_loss_20: 1.8157 - dense_1_loss_21: 1.8963 - dense_1_loss_22: 1.7866 - dense_1_loss_23: 1.8891 - dense_1_loss_24: 1.8838 - dense_1_loss_25: 1.9927 - dense_1_loss_26: 1.7482 - dense_1_loss_27: 1.9381 - dense_1_loss_28: 1.8608 - dense_1_loss_29: 1.7845 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2500 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.3000 - dense_1_acc_5: 0.3333 - dense_1_acc_6: 0.3833 - dense_1_acc_7: 0.4500 - dense_1_acc_8: 0.4833 - dense_1_acc_9: 0.4667 - dense_1_acc_10: 0.5000 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.4667 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5667 - dense_1_acc_15: 0.4833 - dense_1_acc_16: 0.4167 - dense_1_acc_17: 0.4667 - dense_1_acc_18: 0.5000 - dense_1_acc_19: 0.5500 - dense_1_acc_20: 0.5333 - dense_1_acc_21: 0.4167 - dense_1_acc_22: 0.4833 - dense_1_acc_23: 0.4833 - dense_1_acc_24: 0.4667 - dense_1_acc_25: 0.3667 - dense_1_acc_26: 0.5500 - dense_1_acc_27: 0.5167 - dense_1_acc_28: 0.4667 - dense_1_acc_29: 0.5333 - dense_1_acc_30: 0.0000e+00     
Epoch 20/100
60/60 [==============================] - 0s - loss: 58.7664 - dense_1_loss_1: 4.1225 - dense_1_loss_2: 3.6111 - dense_1_loss_3: 2.9434 - dense_1_loss_4: 2.6590 - dense_1_loss_5: 2.2555 - dense_1_loss_6: 2.1823 - dense_1_loss_7: 2.0291 - dense_1_loss_8: 1.8497 - dense_1_loss_9: 1.9002 - dense_1_loss_10: 1.7887 - dense_1_loss_11: 1.9562 - dense_1_loss_12: 1.8061 - dense_1_loss_13: 1.6122 - dense_1_loss_14: 1.6547 - dense_1_loss_15: 1.8340 - dense_1_loss_16: 1.8936 - dense_1_loss_17: 1.7643 - dense_1_loss_18: 1.7598 - dense_1_loss_19: 1.7643 - dense_1_loss_20: 1.6957 - dense_1_loss_21: 1.7188 - dense_1_loss_22: 1.6864 - dense_1_loss_23: 1.7954 - dense_1_loss_24: 1.7385 - dense_1_loss_25: 1.8817 - dense_1_loss_26: 1.6355 - dense_1_loss_27: 1.8235 - dense_1_loss_28: 1.7193 - dense_1_loss_29: 1.6851 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.2833 - dense_1_acc_4: 0.2833 - dense_1_acc_5: 0.3500 - dense_1_acc_6: 0.4000 - dense_1_acc_7: 0.4667 - dense_1_acc_8: 0.4833 - dense_1_acc_9: 0.5500 - dense_1_acc_10: 0.4833 - dense_1_acc_11: 0.3833 - dense_1_acc_12: 0.5000 - dense_1_acc_13: 0.6000 - dense_1_acc_14: 0.5500 - dense_1_acc_15: 0.4500 - dense_1_acc_16: 0.4833 - dense_1_acc_17: 0.4500 - dense_1_acc_18: 0.4833 - dense_1_acc_19: 0.5667 - dense_1_acc_20: 0.5167 - dense_1_acc_21: 0.4667 - dense_1_acc_22: 0.5833 - dense_1_acc_23: 0.5000 - dense_1_acc_24: 0.5000 - dense_1_acc_25: 0.4500 - dense_1_acc_26: 0.6333 - dense_1_acc_27: 0.5000 - dense_1_acc_28: 0.5333 - dense_1_acc_29: 0.5667 - dense_1_acc_30: 0.0000e+00     
Epoch 21/100
60/60 [==============================] - 0s - loss: 55.8111 - dense_1_loss_1: 4.1131 - dense_1_loss_2: 3.5597 - dense_1_loss_3: 2.8458 - dense_1_loss_4: 2.5420 - dense_1_loss_5: 2.1434 - dense_1_loss_6: 2.0811 - dense_1_loss_7: 1.9279 - dense_1_loss_8: 1.7501 - dense_1_loss_9: 1.7831 - dense_1_loss_10: 1.6940 - dense_1_loss_11: 1.8732 - dense_1_loss_12: 1.7151 - dense_1_loss_13: 1.5172 - dense_1_loss_14: 1.5594 - dense_1_loss_15: 1.7414 - dense_1_loss_16: 1.7697 - dense_1_loss_17: 1.6523 - dense_1_loss_18: 1.6534 - dense_1_loss_19: 1.6363 - dense_1_loss_20: 1.6081 - dense_1_loss_21: 1.6131 - dense_1_loss_22: 1.6202 - dense_1_loss_23: 1.6642 - dense_1_loss_24: 1.6335 - dense_1_loss_25: 1.7535 - dense_1_loss_26: 1.4990 - dense_1_loss_27: 1.6664 - dense_1_loss_28: 1.6164 - dense_1_loss_29: 1.5782 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.3167 - dense_1_acc_5: 0.4000 - dense_1_acc_6: 0.4500 - dense_1_acc_7: 0.5000 - dense_1_acc_8: 0.5833 - dense_1_acc_9: 0.5333 - dense_1_acc_10: 0.5167 - dense_1_acc_11: 0.4000 - dense_1_acc_12: 0.5000 - dense_1_acc_13: 0.5833 - dense_1_acc_14: 0.5500 - dense_1_acc_15: 0.5000 - dense_1_acc_16: 0.4667 - dense_1_acc_17: 0.6167 - dense_1_acc_18: 0.5667 - dense_1_acc_19: 0.5667 - dense_1_acc_20: 0.5500 - dense_1_acc_21: 0.5333 - dense_1_acc_22: 0.6500 - dense_1_acc_23: 0.6167 - dense_1_acc_24: 0.5667 - dense_1_acc_25: 0.4333 - dense_1_acc_26: 0.7167 - dense_1_acc_27: 0.6500 - dense_1_acc_28: 0.5833 - dense_1_acc_29: 0.7000 - dense_1_acc_30: 0.0000e+00     
Epoch 22/100
60/60 [==============================] - 0s - loss: 52.8732 - dense_1_loss_1: 4.1042 - dense_1_loss_2: 3.5090 - dense_1_loss_3: 2.7507 - dense_1_loss_4: 2.4376 - dense_1_loss_5: 2.0403 - dense_1_loss_6: 1.9821 - dense_1_loss_7: 1.8094 - dense_1_loss_8: 1.6636 - dense_1_loss_9: 1.7046 - dense_1_loss_10: 1.5742 - dense_1_loss_11: 1.7075 - dense_1_loss_12: 1.6470 - dense_1_loss_13: 1.4566 - dense_1_loss_14: 1.4937 - dense_1_loss_15: 1.6134 - dense_1_loss_16: 1.6845 - dense_1_loss_17: 1.5134 - dense_1_loss_18: 1.5170 - dense_1_loss_19: 1.5788 - dense_1_loss_20: 1.4947 - dense_1_loss_21: 1.5108 - dense_1_loss_22: 1.5031 - dense_1_loss_23: 1.5122 - dense_1_loss_24: 1.5455 - dense_1_loss_25: 1.5979 - dense_1_loss_26: 1.3900 - dense_1_loss_27: 1.5286 - dense_1_loss_28: 1.5239 - dense_1_loss_29: 1.4788 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.3333 - dense_1_acc_5: 0.4000 - dense_1_acc_6: 0.5000 - dense_1_acc_7: 0.5167 - dense_1_acc_8: 0.5667 - dense_1_acc_9: 0.5500 - dense_1_acc_10: 0.5833 - dense_1_acc_11: 0.4500 - dense_1_acc_12: 0.5167 - dense_1_acc_13: 0.6333 - dense_1_acc_14: 0.6000 - dense_1_acc_15: 0.5500 - dense_1_acc_16: 0.4667 - dense_1_acc_17: 0.6000 - dense_1_acc_18: 0.6167 - dense_1_acc_19: 0.6000 - dense_1_acc_20: 0.5667 - dense_1_acc_21: 0.5500 - dense_1_acc_22: 0.5833 - dense_1_acc_23: 0.6833 - dense_1_acc_24: 0.5833 - dense_1_acc_25: 0.4667 - dense_1_acc_26: 0.6500 - dense_1_acc_27: 0.6667 - dense_1_acc_28: 0.6333 - dense_1_acc_29: 0.6833 - dense_1_acc_30: 0.0000e+00     
Epoch 23/100
60/60 [==============================] - 0s - loss: 50.0607 - dense_1_loss_1: 4.0949 - dense_1_loss_2: 3.4588 - dense_1_loss_3: 2.6533 - dense_1_loss_4: 2.3219 - dense_1_loss_5: 1.9310 - dense_1_loss_6: 1.8721 - dense_1_loss_7: 1.6809 - dense_1_loss_8: 1.5700 - dense_1_loss_9: 1.6050 - dense_1_loss_10: 1.4599 - dense_1_loss_11: 1.6121 - dense_1_loss_12: 1.5133 - dense_1_loss_13: 1.3585 - dense_1_loss_14: 1.3967 - dense_1_loss_15: 1.4876 - dense_1_loss_16: 1.5302 - dense_1_loss_17: 1.3841 - dense_1_loss_18: 1.4144 - dense_1_loss_19: 1.4589 - dense_1_loss_20: 1.4110 - dense_1_loss_21: 1.4176 - dense_1_loss_22: 1.4214 - dense_1_loss_23: 1.4592 - dense_1_loss_24: 1.4557 - dense_1_loss_25: 1.4918 - dense_1_loss_26: 1.2951 - dense_1_loss_27: 1.4786 - dense_1_loss_28: 1.4501 - dense_1_loss_29: 1.3766 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.3167 - dense_1_acc_4: 0.3667 - dense_1_acc_5: 0.4167 - dense_1_acc_6: 0.5167 - dense_1_acc_7: 0.6000 - dense_1_acc_8: 0.6000 - dense_1_acc_9: 0.5833 - dense_1_acc_10: 0.7000 - dense_1_acc_11: 0.5333 - dense_1_acc_12: 0.6833 - dense_1_acc_13: 0.7833 - dense_1_acc_14: 0.7000 - dense_1_acc_15: 0.7000 - dense_1_acc_16: 0.5833 - dense_1_acc_17: 0.7167 - dense_1_acc_18: 0.6667 - dense_1_acc_19: 0.7167 - dense_1_acc_20: 0.6333 - dense_1_acc_21: 0.6167 - dense_1_acc_22: 0.7167 - dense_1_acc_23: 0.7167 - dense_1_acc_24: 0.6333 - dense_1_acc_25: 0.6000 - dense_1_acc_26: 0.7833 - dense_1_acc_27: 0.6833 - dense_1_acc_28: 0.7167 - dense_1_acc_29: 0.7500 - dense_1_acc_30: 0.0000e+00     
Epoch 24/100
60/60 [==============================] - 0s - loss: 47.3409 - dense_1_loss_1: 4.0864 - dense_1_loss_2: 3.4101 - dense_1_loss_3: 2.5604 - dense_1_loss_4: 2.2118 - dense_1_loss_5: 1.8485 - dense_1_loss_6: 1.7812 - dense_1_loss_7: 1.5718 - dense_1_loss_8: 1.4623 - dense_1_loss_9: 1.5027 - dense_1_loss_10: 1.3697 - dense_1_loss_11: 1.4926 - dense_1_loss_12: 1.4171 - dense_1_loss_13: 1.2442 - dense_1_loss_14: 1.2531 - dense_1_loss_15: 1.3906 - dense_1_loss_16: 1.4423 - dense_1_loss_17: 1.2674 - dense_1_loss_18: 1.2905 - dense_1_loss_19: 1.3475 - dense_1_loss_20: 1.3441 - dense_1_loss_21: 1.3117 - dense_1_loss_22: 1.3469 - dense_1_loss_23: 1.3530 - dense_1_loss_24: 1.3644 - dense_1_loss_25: 1.4253 - dense_1_loss_26: 1.2301 - dense_1_loss_27: 1.3572 - dense_1_loss_28: 1.3515 - dense_1_loss_29: 1.3065 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.3500 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.4500 - dense_1_acc_6: 0.5333 - dense_1_acc_7: 0.7167 - dense_1_acc_8: 0.6667 - dense_1_acc_9: 0.6333 - dense_1_acc_10: 0.7500 - dense_1_acc_11: 0.6500 - dense_1_acc_12: 0.6667 - dense_1_acc_13: 0.8000 - dense_1_acc_14: 0.8333 - dense_1_acc_15: 0.7333 - dense_1_acc_16: 0.6167 - dense_1_acc_17: 0.7333 - dense_1_acc_18: 0.6833 - dense_1_acc_19: 0.6500 - dense_1_acc_20: 0.6833 - dense_1_acc_21: 0.6000 - dense_1_acc_22: 0.7000 - dense_1_acc_23: 0.7833 - dense_1_acc_24: 0.7333 - dense_1_acc_25: 0.6500 - dense_1_acc_26: 0.7667 - dense_1_acc_27: 0.7000 - dense_1_acc_28: 0.7333 - dense_1_acc_29: 0.7833 - dense_1_acc_30: 0.0000e+00     
Epoch 25/100
60/60 [==============================] - 0s - loss: 44.7013 - dense_1_loss_1: 4.0776 - dense_1_loss_2: 3.3578 - dense_1_loss_3: 2.4685 - dense_1_loss_4: 2.1091 - dense_1_loss_5: 1.7556 - dense_1_loss_6: 1.6894 - dense_1_loss_7: 1.4493 - dense_1_loss_8: 1.3633 - dense_1_loss_9: 1.4131 - dense_1_loss_10: 1.2715 - dense_1_loss_11: 1.3894 - dense_1_loss_12: 1.3141 - dense_1_loss_13: 1.1653 - dense_1_loss_14: 1.1615 - dense_1_loss_15: 1.2728 - dense_1_loss_16: 1.3469 - dense_1_loss_17: 1.2110 - dense_1_loss_18: 1.1638 - dense_1_loss_19: 1.2753 - dense_1_loss_20: 1.2554 - dense_1_loss_21: 1.2457 - dense_1_loss_22: 1.2842 - dense_1_loss_23: 1.2240 - dense_1_loss_24: 1.2583 - dense_1_loss_25: 1.3302 - dense_1_loss_26: 1.1740 - dense_1_loss_27: 1.2470 - dense_1_loss_28: 1.2420 - dense_1_loss_29: 1.1851 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.2833 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.3833 - dense_1_acc_5: 0.5167 - dense_1_acc_6: 0.5500 - dense_1_acc_7: 0.7833 - dense_1_acc_8: 0.7500 - dense_1_acc_9: 0.7333 - dense_1_acc_10: 0.7833 - dense_1_acc_11: 0.6667 - dense_1_acc_12: 0.7333 - dense_1_acc_13: 0.8833 - dense_1_acc_14: 0.8833 - dense_1_acc_15: 0.7667 - dense_1_acc_16: 0.7000 - dense_1_acc_17: 0.7667 - dense_1_acc_18: 0.8333 - dense_1_acc_19: 0.7500 - dense_1_acc_20: 0.7667 - dense_1_acc_21: 0.7000 - dense_1_acc_22: 0.7667 - dense_1_acc_23: 0.8333 - dense_1_acc_24: 0.7000 - dense_1_acc_25: 0.6167 - dense_1_acc_26: 0.7833 - dense_1_acc_27: 0.7000 - dense_1_acc_28: 0.7667 - dense_1_acc_29: 0.8500 - dense_1_acc_30: 0.0000e+00     
Epoch 26/100
60/60 [==============================] - 0s - loss: 42.2567 - dense_1_loss_1: 4.0695 - dense_1_loss_2: 3.3050 - dense_1_loss_3: 2.3785 - dense_1_loss_4: 2.0059 - dense_1_loss_5: 1.6732 - dense_1_loss_6: 1.5827 - dense_1_loss_7: 1.3312 - dense_1_loss_8: 1.2648 - dense_1_loss_9: 1.3399 - dense_1_loss_10: 1.1814 - dense_1_loss_11: 1.2921 - dense_1_loss_12: 1.1893 - dense_1_loss_13: 1.0802 - dense_1_loss_14: 1.0993 - dense_1_loss_15: 1.1858 - dense_1_loss_16: 1.2184 - dense_1_loss_17: 1.1338 - dense_1_loss_18: 1.0866 - dense_1_loss_19: 1.1951 - dense_1_loss_20: 1.1840 - dense_1_loss_21: 1.1575 - dense_1_loss_22: 1.1866 - dense_1_loss_23: 1.1434 - dense_1_loss_24: 1.1996 - dense_1_loss_25: 1.2470 - dense_1_loss_26: 1.0859 - dense_1_loss_27: 1.1441 - dense_1_loss_28: 1.1863 - dense_1_loss_29: 1.1095 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.4000 - dense_1_acc_5: 0.5500 - dense_1_acc_6: 0.6167 - dense_1_acc_7: 0.8333 - dense_1_acc_8: 0.8500 - dense_1_acc_9: 0.7333 - dense_1_acc_10: 0.7833 - dense_1_acc_11: 0.6833 - dense_1_acc_12: 0.7667 - dense_1_acc_13: 0.9000 - dense_1_acc_14: 0.8000 - dense_1_acc_15: 0.7167 - dense_1_acc_16: 0.7667 - dense_1_acc_17: 0.8500 - dense_1_acc_18: 0.8500 - dense_1_acc_19: 0.7833 - dense_1_acc_20: 0.8667 - dense_1_acc_21: 0.7833 - dense_1_acc_22: 0.8167 - dense_1_acc_23: 0.8000 - dense_1_acc_24: 0.7333 - dense_1_acc_25: 0.6833 - dense_1_acc_26: 0.8000 - dense_1_acc_27: 0.7500 - dense_1_acc_28: 0.7667 - dense_1_acc_29: 0.8667 - dense_1_acc_30: 0.0000e+00     
Epoch 27/100
60/60 [==============================] - 0s - loss: 39.8966 - dense_1_loss_1: 4.0606 - dense_1_loss_2: 3.2516 - dense_1_loss_3: 2.2970 - dense_1_loss_4: 1.9132 - dense_1_loss_5: 1.5928 - dense_1_loss_6: 1.5095 - dense_1_loss_7: 1.2444 - dense_1_loss_8: 1.1748 - dense_1_loss_9: 1.2530 - dense_1_loss_10: 1.0967 - dense_1_loss_11: 1.1914 - dense_1_loss_12: 1.0949 - dense_1_loss_13: 1.0060 - dense_1_loss_14: 1.0330 - dense_1_loss_15: 1.0954 - dense_1_loss_16: 1.1213 - dense_1_loss_17: 1.0378 - dense_1_loss_18: 1.0475 - dense_1_loss_19: 1.0845 - dense_1_loss_20: 1.1116 - dense_1_loss_21: 1.1095 - dense_1_loss_22: 1.0690 - dense_1_loss_23: 1.0527 - dense_1_loss_24: 1.1137 - dense_1_loss_25: 1.1929 - dense_1_loss_26: 0.9749 - dense_1_loss_27: 1.0386 - dense_1_loss_28: 1.1031 - dense_1_loss_29: 1.0254 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4000 - dense_1_acc_4: 0.4167 - dense_1_acc_5: 0.5667 - dense_1_acc_6: 0.6500 - dense_1_acc_7: 0.8500 - dense_1_acc_8: 0.8667 - dense_1_acc_9: 0.7167 - dense_1_acc_10: 0.7833 - dense_1_acc_11: 0.7500 - dense_1_acc_12: 0.8000 - dense_1_acc_13: 0.9167 - dense_1_acc_14: 0.8333 - dense_1_acc_15: 0.7500 - dense_1_acc_16: 0.7833 - dense_1_acc_17: 0.8333 - dense_1_acc_18: 0.8833 - dense_1_acc_19: 0.8333 - dense_1_acc_20: 0.7833 - dense_1_acc_21: 0.7667 - dense_1_acc_22: 0.8000 - dense_1_acc_23: 0.8667 - dense_1_acc_24: 0.7333 - dense_1_acc_25: 0.6500 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.8167 - dense_1_acc_28: 0.7500 - dense_1_acc_29: 0.8333 - dense_1_acc_30: 0.0000e+00     
Epoch 28/100
60/60 [==============================] - 0s - loss: 37.6671 - dense_1_loss_1: 4.0519 - dense_1_loss_2: 3.1983 - dense_1_loss_3: 2.2140 - dense_1_loss_4: 1.8196 - dense_1_loss_5: 1.5126 - dense_1_loss_6: 1.4158 - dense_1_loss_7: 1.1428 - dense_1_loss_8: 1.1171 - dense_1_loss_9: 1.1531 - dense_1_loss_10: 1.0175 - dense_1_loss_11: 1.0955 - dense_1_loss_12: 1.0085 - dense_1_loss_13: 0.9224 - dense_1_loss_14: 0.9253 - dense_1_loss_15: 1.0148 - dense_1_loss_16: 1.0345 - dense_1_loss_17: 0.9589 - dense_1_loss_18: 0.9771 - dense_1_loss_19: 1.0180 - dense_1_loss_20: 1.0323 - dense_1_loss_21: 1.0083 - dense_1_loss_22: 1.0083 - dense_1_loss_23: 1.0013 - dense_1_loss_24: 1.0371 - dense_1_loss_25: 1.1049 - dense_1_loss_26: 0.8977 - dense_1_loss_27: 0.9806 - dense_1_loss_28: 1.0336 - dense_1_loss_29: 0.9655 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3000 - dense_1_acc_3: 0.4167 - dense_1_acc_4: 0.5167 - dense_1_acc_5: 0.6167 - dense_1_acc_6: 0.6667 - dense_1_acc_7: 0.8833 - dense_1_acc_8: 0.9333 - dense_1_acc_9: 0.7500 - dense_1_acc_10: 0.8167 - dense_1_acc_11: 0.8500 - dense_1_acc_12: 0.9167 - dense_1_acc_13: 0.9333 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.8500 - dense_1_acc_16: 0.8833 - dense_1_acc_17: 0.9000 - dense_1_acc_18: 0.9000 - dense_1_acc_19: 0.8500 - dense_1_acc_20: 0.8500 - dense_1_acc_21: 0.8667 - dense_1_acc_22: 0.8667 - dense_1_acc_23: 0.9000 - dense_1_acc_24: 0.7500 - dense_1_acc_25: 0.7500 - dense_1_acc_26: 0.9167 - dense_1_acc_27: 0.9000 - dense_1_acc_28: 0.8167 - dense_1_acc_29: 0.8167 - dense_1_acc_30: 0.0000e+00     
Epoch 29/100
60/60 [==============================] - 0s - loss: 35.4806 - dense_1_loss_1: 4.0437 - dense_1_loss_2: 3.1461 - dense_1_loss_3: 2.1346 - dense_1_loss_4: 1.7317 - dense_1_loss_5: 1.4356 - dense_1_loss_6: 1.3289 - dense_1_loss_7: 1.0583 - dense_1_loss_8: 1.0255 - dense_1_loss_9: 1.0718 - dense_1_loss_10: 0.9273 - dense_1_loss_11: 0.9954 - dense_1_loss_12: 0.9304 - dense_1_loss_13: 0.8649 - dense_1_loss_14: 0.8606 - dense_1_loss_15: 0.9263 - dense_1_loss_16: 0.9372 - dense_1_loss_17: 0.8940 - dense_1_loss_18: 0.8995 - dense_1_loss_19: 0.9430 - dense_1_loss_20: 0.9693 - dense_1_loss_21: 0.9241 - dense_1_loss_22: 0.9329 - dense_1_loss_23: 0.9216 - dense_1_loss_24: 0.9559 - dense_1_loss_25: 1.0254 - dense_1_loss_26: 0.8244 - dense_1_loss_27: 0.9306 - dense_1_loss_28: 0.9615 - dense_1_loss_29: 0.8804 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.4500 - dense_1_acc_4: 0.5667 - dense_1_acc_5: 0.6667 - dense_1_acc_6: 0.6833 - dense_1_acc_7: 0.8833 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.7833 - dense_1_acc_10: 0.8667 - dense_1_acc_11: 0.8833 - dense_1_acc_12: 0.9000 - dense_1_acc_13: 0.9333 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.9000 - dense_1_acc_16: 0.9500 - dense_1_acc_17: 0.9333 - dense_1_acc_18: 0.9333 - dense_1_acc_19: 0.8167 - dense_1_acc_20: 0.9167 - dense_1_acc_21: 0.9167 - dense_1_acc_22: 0.9333 - dense_1_acc_23: 0.9000 - dense_1_acc_24: 0.7833 - dense_1_acc_25: 0.8000 - dense_1_acc_26: 0.9500 - dense_1_acc_27: 0.9167 - dense_1_acc_28: 0.8667 - dense_1_acc_29: 0.8833 - dense_1_acc_30: 0.0000e+00     
Epoch 30/100
60/60 [==============================] - 0s - loss: 33.4358 - dense_1_loss_1: 4.0352 - dense_1_loss_2: 3.0906 - dense_1_loss_3: 2.0557 - dense_1_loss_4: 1.6425 - dense_1_loss_5: 1.3591 - dense_1_loss_6: 1.2418 - dense_1_loss_7: 0.9789 - dense_1_loss_8: 0.9662 - dense_1_loss_9: 0.9799 - dense_1_loss_10: 0.8408 - dense_1_loss_11: 0.9129 - dense_1_loss_12: 0.8549 - dense_1_loss_13: 0.7975 - dense_1_loss_14: 0.7937 - dense_1_loss_15: 0.8607 - dense_1_loss_16: 0.8759 - dense_1_loss_17: 0.8134 - dense_1_loss_18: 0.8121 - dense_1_loss_19: 0.8673 - dense_1_loss_20: 0.9116 - dense_1_loss_21: 0.8690 - dense_1_loss_22: 0.8641 - dense_1_loss_23: 0.8488 - dense_1_loss_24: 0.8774 - dense_1_loss_25: 0.9345 - dense_1_loss_26: 0.7932 - dense_1_loss_27: 0.8554 - dense_1_loss_28: 0.8819 - dense_1_loss_29: 0.8204 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.5167 - dense_1_acc_4: 0.5833 - dense_1_acc_5: 0.6667 - dense_1_acc_6: 0.7000 - dense_1_acc_7: 0.9167 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.9333 - dense_1_acc_11: 0.8667 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 0.9333 - dense_1_acc_14: 0.9500 - dense_1_acc_15: 0.9500 - dense_1_acc_16: 0.9667 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 0.9667 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9333 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9333 - dense_1_acc_24: 0.8833 - dense_1_acc_25: 0.8833 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 0.9500 - dense_1_acc_28: 0.8833 - dense_1_acc_29: 0.9000 - dense_1_acc_30: 0.0000e+00     
Epoch 31/100
60/60 [==============================] - 0s - loss: 31.4864 - dense_1_loss_1: 4.0263 - dense_1_loss_2: 3.0406 - dense_1_loss_3: 1.9777 - dense_1_loss_4: 1.5551 - dense_1_loss_5: 1.2808 - dense_1_loss_6: 1.1512 - dense_1_loss_7: 0.9090 - dense_1_loss_8: 0.8973 - dense_1_loss_9: 0.8901 - dense_1_loss_10: 0.7774 - dense_1_loss_11: 0.8347 - dense_1_loss_12: 0.7892 - dense_1_loss_13: 0.7435 - dense_1_loss_14: 0.7311 - dense_1_loss_15: 0.7884 - dense_1_loss_16: 0.7992 - dense_1_loss_17: 0.7514 - dense_1_loss_18: 0.7430 - dense_1_loss_19: 0.7921 - dense_1_loss_20: 0.8654 - dense_1_loss_21: 0.7843 - dense_1_loss_22: 0.8028 - dense_1_loss_23: 0.7862 - dense_1_loss_24: 0.8180 - dense_1_loss_25: 0.8576 - dense_1_loss_26: 0.7297 - dense_1_loss_27: 0.7687 - dense_1_loss_28: 0.8191 - dense_1_loss_29: 0.7763 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.5333 - dense_1_acc_4: 0.6167 - dense_1_acc_5: 0.6833 - dense_1_acc_6: 0.7333 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.9500 - dense_1_acc_11: 0.9167 - dense_1_acc_12: 0.9500 - dense_1_acc_13: 0.9500 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 0.9833 - dense_1_acc_17: 0.9667 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9000 - dense_1_acc_20: 0.9500 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9167 - dense_1_acc_24: 0.9000 - dense_1_acc_25: 0.9000 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 0.9667 - dense_1_acc_28: 0.9167 - dense_1_acc_29: 0.9167 - dense_1_acc_30: 0.0000e+00     
Epoch 32/100
60/60 [==============================] - 0s - loss: 29.6126 - dense_1_loss_1: 4.0183 - dense_1_loss_2: 2.9891 - dense_1_loss_3: 1.9021 - dense_1_loss_4: 1.4747 - dense_1_loss_5: 1.2094 - dense_1_loss_6: 1.0751 - dense_1_loss_7: 0.8455 - dense_1_loss_8: 0.8266 - dense_1_loss_9: 0.8259 - dense_1_loss_10: 0.7142 - dense_1_loss_11: 0.7642 - dense_1_loss_12: 0.7169 - dense_1_loss_13: 0.6770 - dense_1_loss_14: 0.6623 - dense_1_loss_15: 0.7297 - dense_1_loss_16: 0.7233 - dense_1_loss_17: 0.6909 - dense_1_loss_18: 0.6941 - dense_1_loss_19: 0.7171 - dense_1_loss_20: 0.7974 - dense_1_loss_21: 0.7255 - dense_1_loss_22: 0.7221 - dense_1_loss_23: 0.7280 - dense_1_loss_24: 0.7603 - dense_1_loss_25: 0.7931 - dense_1_loss_26: 0.6608 - dense_1_loss_27: 0.6963 - dense_1_loss_28: 0.7588 - dense_1_loss_29: 0.7139 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.5500 - dense_1_acc_4: 0.6167 - dense_1_acc_5: 0.6833 - dense_1_acc_6: 0.7333 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.8667 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 0.9833 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 0.9833 - dense_1_acc_19: 0.9333 - dense_1_acc_20: 0.9833 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9333 - dense_1_acc_24: 0.9500 - dense_1_acc_25: 0.9000 - dense_1_acc_26: 0.9667 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9500 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 33/100
60/60 [==============================] - 0s - loss: 27.8691 - dense_1_loss_1: 4.0111 - dense_1_loss_2: 2.9362 - dense_1_loss_3: 1.8298 - dense_1_loss_4: 1.3923 - dense_1_loss_5: 1.1346 - dense_1_loss_6: 0.9925 - dense_1_loss_7: 0.7918 - dense_1_loss_8: 0.7579 - dense_1_loss_9: 0.7528 - dense_1_loss_10: 0.6623 - dense_1_loss_11: 0.7116 - dense_1_loss_12: 0.6578 - dense_1_loss_13: 0.6141 - dense_1_loss_14: 0.5953 - dense_1_loss_15: 0.6683 - dense_1_loss_16: 0.6558 - dense_1_loss_17: 0.6434 - dense_1_loss_18: 0.6375 - dense_1_loss_19: 0.6439 - dense_1_loss_20: 0.7264 - dense_1_loss_21: 0.6724 - dense_1_loss_22: 0.6739 - dense_1_loss_23: 0.6764 - dense_1_loss_24: 0.6981 - dense_1_loss_25: 0.7310 - dense_1_loss_26: 0.5996 - dense_1_loss_27: 0.6418 - dense_1_loss_28: 0.7021 - dense_1_loss_29: 0.6585 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3333 - dense_1_acc_3: 0.6000 - dense_1_acc_4: 0.6167 - dense_1_acc_5: 0.7167 - dense_1_acc_6: 0.7333 - dense_1_acc_7: 0.9333 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9000 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.9333 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 0.9833 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 0.9667 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 0.9833 - dense_1_acc_23: 0.9333 - dense_1_acc_24: 0.9667 - dense_1_acc_25: 0.9167 - dense_1_acc_26: 0.9833 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9500 - dense_1_acc_30: 0.0000e+00     
Epoch 34/100
60/60 [==============================] - 0s - loss: 26.2206 - dense_1_loss_1: 4.0031 - dense_1_loss_2: 2.8862 - dense_1_loss_3: 1.7591 - dense_1_loss_4: 1.3174 - dense_1_loss_5: 1.0656 - dense_1_loss_6: 0.9200 - dense_1_loss_7: 0.7393 - dense_1_loss_8: 0.6883 - dense_1_loss_9: 0.7049 - dense_1_loss_10: 0.5939 - dense_1_loss_11: 0.6465 - dense_1_loss_12: 0.6050 - dense_1_loss_13: 0.5581 - dense_1_loss_14: 0.5435 - dense_1_loss_15: 0.6139 - dense_1_loss_16: 0.6018 - dense_1_loss_17: 0.5927 - dense_1_loss_18: 0.5788 - dense_1_loss_19: 0.6030 - dense_1_loss_20: 0.6778 - dense_1_loss_21: 0.6272 - dense_1_loss_22: 0.6193 - dense_1_loss_23: 0.5999 - dense_1_loss_24: 0.6418 - dense_1_loss_25: 0.6640 - dense_1_loss_26: 0.5625 - dense_1_loss_27: 0.5771 - dense_1_loss_28: 0.6257 - dense_1_loss_29: 0.6044 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.6333 - dense_1_acc_5: 0.7667 - dense_1_acc_6: 0.8167 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.9667 - dense_1_acc_9: 0.9167 - dense_1_acc_10: 0.9667 - dense_1_acc_11: 0.9667 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - dense_1_acc_15: 1.0000 - dense_1_acc_16: 1.0000 - dense_1_acc_17: 0.9833 - dense_1_acc_18: 1.0000 - dense_1_acc_19: 1.0000 - dense_1_acc_20: 1.0000 - dense_1_acc_21: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9333 - 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: 24.7092 - dense_1_loss_1: 3.9960 - dense_1_loss_2: 2.8342 - dense_1_loss_3: 1.6882 - dense_1_loss_4: 1.2415 - dense_1_loss_5: 1.0031 - dense_1_loss_6: 0.8428 - dense_1_loss_7: 0.6821 - dense_1_loss_8: 0.6273 - dense_1_loss_9: 0.6421 - dense_1_loss_10: 0.5508 - dense_1_loss_11: 0.5777 - dense_1_loss_12: 0.5519 - dense_1_loss_13: 0.4971 - dense_1_loss_14: 0.5048 - dense_1_loss_15: 0.5514 - dense_1_loss_16: 0.5328 - dense_1_loss_17: 0.5480 - dense_1_loss_18: 0.5393 - dense_1_loss_19: 0.5387 - dense_1_loss_20: 0.6138 - dense_1_loss_21: 0.5738 - dense_1_loss_22: 0.5848 - dense_1_loss_23: 0.5761 - dense_1_loss_24: 0.5793 - dense_1_loss_25: 0.6147 - dense_1_loss_26: 0.5086 - dense_1_loss_27: 0.5313 - dense_1_loss_28: 0.6005 - dense_1_loss_29: 0.5765 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6167 - dense_1_acc_4: 0.6833 - dense_1_acc_5: 0.7833 - dense_1_acc_6: 0.8500 - dense_1_acc_7: 0.9500 - dense_1_acc_8: 0.9500 - dense_1_acc_9: 0.9333 - dense_1_acc_10: 0.9833 - dense_1_acc_11: 1.0000 - dense_1_acc_12: 0.9667 - dense_1_acc_13: 1.0000 - dense_1_acc_14: 0.9833 - 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: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - 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 36/100
60/60 [==============================] - 0s - loss: 23.2193 - dense_1_loss_1: 3.9893 - dense_1_loss_2: 2.7844 - dense_1_loss_3: 1.6245 - dense_1_loss_4: 1.1708 - dense_1_loss_5: 0.9360 - dense_1_loss_6: 0.7753 - dense_1_loss_7: 0.6291 - dense_1_loss_8: 0.5816 - dense_1_loss_9: 0.5919 - dense_1_loss_10: 0.4951 - dense_1_loss_11: 0.5268 - dense_1_loss_12: 0.4946 - dense_1_loss_13: 0.4530 - dense_1_loss_14: 0.4574 - dense_1_loss_15: 0.5095 - dense_1_loss_16: 0.4970 - dense_1_loss_17: 0.4965 - dense_1_loss_18: 0.4927 - dense_1_loss_19: 0.4904 - dense_1_loss_20: 0.5665 - dense_1_loss_21: 0.5330 - dense_1_loss_22: 0.5103 - dense_1_loss_23: 0.5154 - dense_1_loss_24: 0.5333 - dense_1_loss_25: 0.5562 - dense_1_loss_26: 0.4624 - dense_1_loss_27: 0.4858 - dense_1_loss_28: 0.5385 - dense_1_loss_29: 0.5219 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3500 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.7000 - dense_1_acc_5: 0.8000 - dense_1_acc_6: 0.8833 - 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: 1.0000 - 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: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 0.9667 - 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 37/100
60/60 [==============================] - 0s - loss: 21.8550 - dense_1_loss_1: 3.9823 - dense_1_loss_2: 2.7340 - dense_1_loss_3: 1.5594 - dense_1_loss_4: 1.1024 - dense_1_loss_5: 0.8717 - dense_1_loss_6: 0.7156 - dense_1_loss_7: 0.5847 - dense_1_loss_8: 0.5280 - dense_1_loss_9: 0.5395 - dense_1_loss_10: 0.4423 - dense_1_loss_11: 0.4906 - dense_1_loss_12: 0.4466 - dense_1_loss_13: 0.4141 - dense_1_loss_14: 0.4091 - dense_1_loss_15: 0.4569 - dense_1_loss_16: 0.4545 - dense_1_loss_17: 0.4523 - dense_1_loss_18: 0.4501 - dense_1_loss_19: 0.4440 - dense_1_loss_20: 0.5119 - dense_1_loss_21: 0.4886 - dense_1_loss_22: 0.4620 - dense_1_loss_23: 0.4685 - dense_1_loss_24: 0.4817 - dense_1_loss_25: 0.5128 - dense_1_loss_26: 0.4179 - dense_1_loss_27: 0.4389 - dense_1_loss_28: 0.5105 - dense_1_loss_29: 0.4842 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.3833 - dense_1_acc_3: 0.6333 - dense_1_acc_4: 0.7167 - dense_1_acc_5: 0.8167 - dense_1_acc_6: 0.8833 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 0.9833 - dense_1_acc_9: 0.9667 - 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: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 0.9833 - dense_1_acc_25: 0.9667 - dense_1_acc_26: 1.0000 - 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 38/100
60/60 [==============================] - 0s - loss: 20.6203 - dense_1_loss_1: 3.9758 - dense_1_loss_2: 2.6861 - dense_1_loss_3: 1.5012 - dense_1_loss_4: 1.0363 - dense_1_loss_5: 0.8102 - dense_1_loss_6: 0.6514 - dense_1_loss_7: 0.5326 - dense_1_loss_8: 0.4808 - dense_1_loss_9: 0.4828 - dense_1_loss_10: 0.4053 - dense_1_loss_11: 0.4421 - dense_1_loss_12: 0.4032 - dense_1_loss_13: 0.3760 - dense_1_loss_14: 0.3781 - dense_1_loss_15: 0.4048 - dense_1_loss_16: 0.4188 - dense_1_loss_17: 0.4118 - dense_1_loss_18: 0.4154 - dense_1_loss_19: 0.4084 - dense_1_loss_20: 0.4635 - dense_1_loss_21: 0.4488 - dense_1_loss_22: 0.4268 - dense_1_loss_23: 0.4401 - dense_1_loss_24: 0.4411 - dense_1_loss_25: 0.4784 - dense_1_loss_26: 0.4037 - dense_1_loss_27: 0.3940 - dense_1_loss_28: 0.4576 - dense_1_loss_29: 0.4449 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.6500 - dense_1_acc_4: 0.7667 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9333 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_22: 1.0000 - dense_1_acc_23: 1.0000 - dense_1_acc_24: 1.0000 - dense_1_acc_25: 0.9833 - dense_1_acc_26: 1.0000 - dense_1_acc_27: 1.0000 - dense_1_acc_28: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 39/100
60/60 [==============================] - 0s - loss: 19.4645 - dense_1_loss_1: 3.9695 - dense_1_loss_2: 2.6370 - dense_1_loss_3: 1.4435 - dense_1_loss_4: 0.9841 - dense_1_loss_5: 0.7496 - dense_1_loss_6: 0.6025 - dense_1_loss_7: 0.4928 - dense_1_loss_8: 0.4467 - dense_1_loss_9: 0.4554 - dense_1_loss_10: 0.3612 - dense_1_loss_11: 0.4065 - dense_1_loss_12: 0.3678 - dense_1_loss_13: 0.3420 - dense_1_loss_14: 0.3445 - dense_1_loss_15: 0.3720 - dense_1_loss_16: 0.3860 - dense_1_loss_17: 0.3814 - dense_1_loss_18: 0.3714 - dense_1_loss_19: 0.3691 - dense_1_loss_20: 0.4148 - dense_1_loss_21: 0.4154 - dense_1_loss_22: 0.3939 - dense_1_loss_23: 0.3877 - dense_1_loss_24: 0.3962 - dense_1_loss_25: 0.4281 - dense_1_loss_26: 0.3589 - dense_1_loss_27: 0.3730 - dense_1_loss_28: 0.4086 - dense_1_loss_29: 0.4049 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.6667 - dense_1_acc_4: 0.7833 - dense_1_acc_5: 0.8833 - dense_1_acc_6: 0.9500 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9667 - 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: 0.9833 - 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 40/100
60/60 [==============================] - 0s - loss: 18.3523 - dense_1_loss_1: 3.9628 - dense_1_loss_2: 2.5903 - dense_1_loss_3: 1.3892 - dense_1_loss_4: 0.9215 - dense_1_loss_5: 0.6950 - dense_1_loss_6: 0.5505 - dense_1_loss_7: 0.4556 - dense_1_loss_8: 0.4075 - dense_1_loss_9: 0.4045 - dense_1_loss_10: 0.3335 - dense_1_loss_11: 0.3509 - dense_1_loss_12: 0.3413 - dense_1_loss_13: 0.3064 - dense_1_loss_14: 0.3065 - dense_1_loss_15: 0.3401 - dense_1_loss_16: 0.3468 - dense_1_loss_17: 0.3433 - dense_1_loss_18: 0.3470 - dense_1_loss_19: 0.3351 - dense_1_loss_20: 0.3824 - dense_1_loss_21: 0.3728 - dense_1_loss_22: 0.3488 - dense_1_loss_23: 0.3532 - dense_1_loss_24: 0.3630 - dense_1_loss_25: 0.3896 - dense_1_loss_26: 0.3313 - dense_1_loss_27: 0.3334 - dense_1_loss_28: 0.3742 - dense_1_loss_29: 0.3757 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.7000 - dense_1_acc_4: 0.8500 - dense_1_acc_5: 0.9000 - dense_1_acc_6: 0.9500 - dense_1_acc_7: 0.9667 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 41/100
60/60 [==============================] - 0s - loss: 17.4048 - dense_1_loss_1: 3.9570 - dense_1_loss_2: 2.5439 - dense_1_loss_3: 1.3364 - dense_1_loss_4: 0.8699 - dense_1_loss_5: 0.6496 - dense_1_loss_6: 0.5023 - dense_1_loss_7: 0.4234 - dense_1_loss_8: 0.3685 - dense_1_loss_9: 0.3686 - dense_1_loss_10: 0.3076 - dense_1_loss_11: 0.3256 - dense_1_loss_12: 0.3122 - dense_1_loss_13: 0.2810 - dense_1_loss_14: 0.2753 - dense_1_loss_15: 0.3134 - dense_1_loss_16: 0.3190 - dense_1_loss_17: 0.3128 - dense_1_loss_18: 0.3175 - dense_1_loss_19: 0.3116 - dense_1_loss_20: 0.3468 - dense_1_loss_21: 0.3371 - dense_1_loss_22: 0.3118 - dense_1_loss_23: 0.3291 - dense_1_loss_24: 0.3304 - dense_1_loss_25: 0.3521 - dense_1_loss_26: 0.3002 - dense_1_loss_27: 0.3058 - dense_1_loss_28: 0.3507 - dense_1_loss_29: 0.3453 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.7167 - dense_1_acc_4: 0.8333 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9667 - 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: 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: 0.9833 - dense_1_acc_29: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 42/100
60/60 [==============================] - 0s - loss: 16.4641 - dense_1_loss_1: 3.9505 - dense_1_loss_2: 2.5017 - dense_1_loss_3: 1.2861 - dense_1_loss_4: 0.8201 - dense_1_loss_5: 0.6052 - dense_1_loss_6: 0.4633 - dense_1_loss_7: 0.3921 - dense_1_loss_8: 0.3460 - dense_1_loss_9: 0.3335 - dense_1_loss_10: 0.2784 - dense_1_loss_11: 0.2921 - dense_1_loss_12: 0.2781 - dense_1_loss_13: 0.2510 - dense_1_loss_14: 0.2451 - dense_1_loss_15: 0.2848 - dense_1_loss_16: 0.2879 - dense_1_loss_17: 0.2845 - dense_1_loss_18: 0.2775 - dense_1_loss_19: 0.2798 - dense_1_loss_20: 0.3145 - dense_1_loss_21: 0.3123 - dense_1_loss_22: 0.2913 - dense_1_loss_23: 0.2909 - dense_1_loss_24: 0.2976 - dense_1_loss_25: 0.3155 - dense_1_loss_26: 0.2737 - dense_1_loss_27: 0.2825 - dense_1_loss_28: 0.3126 - dense_1_loss_29: 0.3155 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4000 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 43/100
60/60 [==============================] - 0s - loss: 15.6618 - dense_1_loss_1: 3.9452 - dense_1_loss_2: 2.4555 - dense_1_loss_3: 1.2406 - dense_1_loss_4: 0.7708 - dense_1_loss_5: 0.5599 - dense_1_loss_6: 0.4310 - dense_1_loss_7: 0.3627 - dense_1_loss_8: 0.3178 - dense_1_loss_9: 0.3049 - dense_1_loss_10: 0.2529 - dense_1_loss_11: 0.2640 - dense_1_loss_12: 0.2581 - dense_1_loss_13: 0.2277 - dense_1_loss_14: 0.2291 - dense_1_loss_15: 0.2588 - dense_1_loss_16: 0.2619 - dense_1_loss_17: 0.2596 - dense_1_loss_18: 0.2547 - dense_1_loss_19: 0.2547 - dense_1_loss_20: 0.2858 - dense_1_loss_21: 0.2878 - dense_1_loss_22: 0.2657 - dense_1_loss_23: 0.2697 - dense_1_loss_24: 0.2703 - dense_1_loss_25: 0.2913 - dense_1_loss_26: 0.2529 - dense_1_loss_27: 0.2559 - dense_1_loss_28: 0.2823 - dense_1_loss_29: 0.2907 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 44/100
60/60 [==============================] - 0s - loss: 14.9305 - dense_1_loss_1: 3.9390 - dense_1_loss_2: 2.4134 - dense_1_loss_3: 1.1971 - dense_1_loss_4: 0.7255 - dense_1_loss_5: 0.5234 - dense_1_loss_6: 0.4026 - dense_1_loss_7: 0.3340 - dense_1_loss_8: 0.2891 - dense_1_loss_9: 0.2818 - dense_1_loss_10: 0.2288 - dense_1_loss_11: 0.2430 - dense_1_loss_12: 0.2386 - dense_1_loss_13: 0.2117 - dense_1_loss_14: 0.2125 - dense_1_loss_15: 0.2358 - dense_1_loss_16: 0.2385 - dense_1_loss_17: 0.2374 - dense_1_loss_18: 0.2345 - dense_1_loss_19: 0.2339 - dense_1_loss_20: 0.2650 - dense_1_loss_21: 0.2632 - dense_1_loss_22: 0.2372 - dense_1_loss_23: 0.2452 - dense_1_loss_24: 0.2490 - dense_1_loss_25: 0.2669 - dense_1_loss_26: 0.2276 - dense_1_loss_27: 0.2364 - dense_1_loss_28: 0.2561 - dense_1_loss_29: 0.2630 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.7333 - dense_1_acc_4: 0.8667 - dense_1_acc_5: 0.9500 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 45/100
60/60 [==============================] - 0s - loss: 14.2457 - dense_1_loss_1: 3.9347 - dense_1_loss_2: 2.3720 - dense_1_loss_3: 1.1526 - dense_1_loss_4: 0.6830 - dense_1_loss_5: 0.4849 - dense_1_loss_6: 0.3750 - dense_1_loss_7: 0.3068 - dense_1_loss_8: 0.2713 - dense_1_loss_9: 0.2508 - dense_1_loss_10: 0.2081 - dense_1_loss_11: 0.2234 - dense_1_loss_12: 0.2156 - dense_1_loss_13: 0.1888 - dense_1_loss_14: 0.1913 - dense_1_loss_15: 0.2149 - dense_1_loss_16: 0.2233 - dense_1_loss_17: 0.2138 - dense_1_loss_18: 0.2144 - dense_1_loss_19: 0.2151 - dense_1_loss_20: 0.2393 - dense_1_loss_21: 0.2432 - dense_1_loss_22: 0.2176 - dense_1_loss_23: 0.2261 - dense_1_loss_24: 0.2294 - dense_1_loss_25: 0.2437 - dense_1_loss_26: 0.2157 - dense_1_loss_27: 0.2152 - dense_1_loss_28: 0.2334 - dense_1_loss_29: 0.2426 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4167 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 46/100
60/60 [==============================] - 0s - loss: 13.6318 - dense_1_loss_1: 3.9285 - dense_1_loss_2: 2.3306 - dense_1_loss_3: 1.1149 - dense_1_loss_4: 0.6411 - dense_1_loss_5: 0.4532 - dense_1_loss_6: 0.3516 - dense_1_loss_7: 0.2846 - dense_1_loss_8: 0.2498 - dense_1_loss_9: 0.2335 - dense_1_loss_10: 0.1924 - dense_1_loss_11: 0.2075 - dense_1_loss_12: 0.1971 - dense_1_loss_13: 0.1723 - dense_1_loss_14: 0.1768 - dense_1_loss_15: 0.1964 - dense_1_loss_16: 0.2018 - dense_1_loss_17: 0.1978 - dense_1_loss_18: 0.1950 - dense_1_loss_19: 0.1949 - dense_1_loss_20: 0.2158 - dense_1_loss_21: 0.2236 - dense_1_loss_22: 0.2031 - dense_1_loss_23: 0.2049 - dense_1_loss_24: 0.2080 - dense_1_loss_25: 0.2218 - dense_1_loss_26: 0.1933 - dense_1_loss_27: 0.1990 - dense_1_loss_28: 0.2175 - dense_1_loss_29: 0.2249 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4333 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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 47/100
60/60 [==============================] - 0s - loss: 13.0783 - dense_1_loss_1: 3.9232 - dense_1_loss_2: 2.2913 - dense_1_loss_3: 1.0794 - dense_1_loss_4: 0.6034 - dense_1_loss_5: 0.4228 - dense_1_loss_6: 0.3301 - dense_1_loss_7: 0.2645 - dense_1_loss_8: 0.2316 - dense_1_loss_9: 0.2173 - dense_1_loss_10: 0.1787 - dense_1_loss_11: 0.1867 - dense_1_loss_12: 0.1831 - dense_1_loss_13: 0.1598 - dense_1_loss_14: 0.1623 - dense_1_loss_15: 0.1846 - dense_1_loss_16: 0.1852 - dense_1_loss_17: 0.1827 - dense_1_loss_18: 0.1777 - dense_1_loss_19: 0.1784 - dense_1_loss_20: 0.2014 - dense_1_loss_21: 0.2040 - dense_1_loss_22: 0.1839 - dense_1_loss_23: 0.1856 - dense_1_loss_24: 0.1909 - dense_1_loss_25: 0.2031 - dense_1_loss_26: 0.1796 - dense_1_loss_27: 0.1800 - dense_1_loss_28: 0.1994 - dense_1_loss_29: 0.2076 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 0.9833 - dense_1_acc_30: 0.0000e+00     
Epoch 48/100
60/60 [==============================] - 0s - loss: 12.5795 - dense_1_loss_1: 3.9182 - dense_1_loss_2: 2.2525 - dense_1_loss_3: 1.0461 - dense_1_loss_4: 0.5689 - dense_1_loss_5: 0.3965 - dense_1_loss_6: 0.3077 - dense_1_loss_7: 0.2436 - dense_1_loss_8: 0.2175 - dense_1_loss_9: 0.1971 - dense_1_loss_10: 0.1651 - dense_1_loss_11: 0.1671 - dense_1_loss_12: 0.1693 - dense_1_loss_13: 0.1471 - dense_1_loss_14: 0.1476 - dense_1_loss_15: 0.1708 - dense_1_loss_16: 0.1752 - dense_1_loss_17: 0.1636 - dense_1_loss_18: 0.1647 - dense_1_loss_19: 0.1647 - dense_1_loss_20: 0.1895 - dense_1_loss_21: 0.1906 - dense_1_loss_22: 0.1654 - dense_1_loss_23: 0.1720 - dense_1_loss_24: 0.1778 - dense_1_loss_25: 0.1916 - dense_1_loss_26: 0.1704 - dense_1_loss_27: 0.1675 - dense_1_loss_28: 0.1787 - dense_1_loss_29: 0.1927 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4500 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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 49/100
60/60 [==============================] - 0s - loss: 12.1086 - dense_1_loss_1: 3.9135 - dense_1_loss_2: 2.2148 - dense_1_loss_3: 1.0115 - dense_1_loss_4: 0.5392 - dense_1_loss_5: 0.3710 - dense_1_loss_6: 0.2864 - dense_1_loss_7: 0.2263 - dense_1_loss_8: 0.2029 - dense_1_loss_9: 0.1844 - dense_1_loss_10: 0.1521 - dense_1_loss_11: 0.1584 - dense_1_loss_12: 0.1553 - dense_1_loss_13: 0.1357 - dense_1_loss_14: 0.1375 - dense_1_loss_15: 0.1552 - dense_1_loss_16: 0.1600 - dense_1_loss_17: 0.1502 - dense_1_loss_18: 0.1543 - dense_1_loss_19: 0.1493 - dense_1_loss_20: 0.1691 - dense_1_loss_21: 0.1752 - dense_1_loss_22: 0.1547 - dense_1_loss_23: 0.1616 - dense_1_loss_24: 0.1620 - dense_1_loss_25: 0.1727 - dense_1_loss_26: 0.1541 - dense_1_loss_27: 0.1575 - dense_1_loss_28: 0.1695 - dense_1_loss_29: 0.1744 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.8833 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9667 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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 50/100
60/60 [==============================] - 0s - loss: 11.6885 - dense_1_loss_1: 3.9085 - dense_1_loss_2: 2.1782 - dense_1_loss_3: 0.9796 - dense_1_loss_4: 0.5068 - dense_1_loss_5: 0.3461 - dense_1_loss_6: 0.2700 - dense_1_loss_7: 0.2108 - dense_1_loss_8: 0.1882 - dense_1_loss_9: 0.1723 - dense_1_loss_10: 0.1387 - dense_1_loss_11: 0.1508 - dense_1_loss_12: 0.1427 - dense_1_loss_13: 0.1245 - dense_1_loss_14: 0.1301 - dense_1_loss_15: 0.1426 - dense_1_loss_16: 0.1476 - dense_1_loss_17: 0.1421 - dense_1_loss_18: 0.1398 - dense_1_loss_19: 0.1403 - dense_1_loss_20: 0.1558 - dense_1_loss_21: 0.1635 - dense_1_loss_22: 0.1442 - dense_1_loss_23: 0.1484 - dense_1_loss_24: 0.1476 - dense_1_loss_25: 0.1610 - dense_1_loss_26: 0.1427 - dense_1_loss_27: 0.1464 - dense_1_loss_28: 0.1550 - dense_1_loss_29: 0.1642 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7500 - dense_1_acc_4: 0.9167 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 11.3062 - dense_1_loss_1: 3.9038 - dense_1_loss_2: 2.1414 - dense_1_loss_3: 0.9511 - dense_1_loss_4: 0.4807 - dense_1_loss_5: 0.3267 - dense_1_loss_6: 0.2544 - dense_1_loss_7: 0.1974 - dense_1_loss_8: 0.1757 - dense_1_loss_9: 0.1599 - dense_1_loss_10: 0.1293 - dense_1_loss_11: 0.1375 - dense_1_loss_12: 0.1333 - dense_1_loss_13: 0.1152 - dense_1_loss_14: 0.1202 - dense_1_loss_15: 0.1328 - dense_1_loss_16: 0.1382 - dense_1_loss_17: 0.1315 - dense_1_loss_18: 0.1285 - dense_1_loss_19: 0.1306 - dense_1_loss_20: 0.1455 - dense_1_loss_21: 0.1507 - dense_1_loss_22: 0.1339 - dense_1_loss_23: 0.1353 - dense_1_loss_24: 0.1379 - dense_1_loss_25: 0.1515 - dense_1_loss_26: 0.1350 - dense_1_loss_27: 0.1344 - dense_1_loss_28: 0.1426 - dense_1_loss_29: 0.1513 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9167 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 10.9622 - dense_1_loss_1: 3.8995 - dense_1_loss_2: 2.1077 - dense_1_loss_3: 0.9226 - dense_1_loss_4: 0.4553 - dense_1_loss_5: 0.3086 - dense_1_loss_6: 0.2398 - dense_1_loss_7: 0.1852 - dense_1_loss_8: 0.1638 - dense_1_loss_9: 0.1495 - dense_1_loss_10: 0.1215 - dense_1_loss_11: 0.1265 - dense_1_loss_12: 0.1243 - dense_1_loss_13: 0.1076 - dense_1_loss_14: 0.1106 - dense_1_loss_15: 0.1248 - dense_1_loss_16: 0.1292 - dense_1_loss_17: 0.1224 - dense_1_loss_18: 0.1197 - dense_1_loss_19: 0.1220 - dense_1_loss_20: 0.1361 - dense_1_loss_21: 0.1411 - dense_1_loss_22: 0.1228 - dense_1_loss_23: 0.1260 - dense_1_loss_24: 0.1291 - dense_1_loss_25: 0.1415 - dense_1_loss_26: 0.1257 - dense_1_loss_27: 0.1265 - dense_1_loss_28: 0.1329 - dense_1_loss_29: 0.1400 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.7667 - dense_1_acc_4: 0.9167 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 10.6455 - dense_1_loss_1: 3.8947 - dense_1_loss_2: 2.0739 - dense_1_loss_3: 0.8952 - dense_1_loss_4: 0.4313 - dense_1_loss_5: 0.2912 - dense_1_loss_6: 0.2270 - dense_1_loss_7: 0.1740 - dense_1_loss_8: 0.1537 - dense_1_loss_9: 0.1414 - dense_1_loss_10: 0.1144 - dense_1_loss_11: 0.1195 - dense_1_loss_12: 0.1155 - dense_1_loss_13: 0.1007 - dense_1_loss_14: 0.1034 - dense_1_loss_15: 0.1164 - dense_1_loss_16: 0.1201 - dense_1_loss_17: 0.1143 - dense_1_loss_18: 0.1117 - dense_1_loss_19: 0.1126 - dense_1_loss_20: 0.1254 - dense_1_loss_21: 0.1322 - dense_1_loss_22: 0.1134 - dense_1_loss_23: 0.1191 - dense_1_loss_24: 0.1200 - dense_1_loss_25: 0.1331 - dense_1_loss_26: 0.1166 - dense_1_loss_27: 0.1195 - dense_1_loss_28: 0.1225 - dense_1_loss_29: 0.1327 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8000 - dense_1_acc_4: 0.9333 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 10.3534 - dense_1_loss_1: 3.8905 - dense_1_loss_2: 2.0416 - dense_1_loss_3: 0.8698 - dense_1_loss_4: 0.4077 - dense_1_loss_5: 0.2733 - dense_1_loss_6: 0.2145 - dense_1_loss_7: 0.1621 - dense_1_loss_8: 0.1453 - dense_1_loss_9: 0.1324 - dense_1_loss_10: 0.1066 - dense_1_loss_11: 0.1130 - dense_1_loss_12: 0.1080 - dense_1_loss_13: 0.0947 - dense_1_loss_14: 0.0972 - dense_1_loss_15: 0.1085 - dense_1_loss_16: 0.1131 - dense_1_loss_17: 0.1077 - dense_1_loss_18: 0.1047 - dense_1_loss_19: 0.1050 - dense_1_loss_20: 0.1163 - dense_1_loss_21: 0.1244 - dense_1_loss_22: 0.1068 - dense_1_loss_23: 0.1120 - dense_1_loss_24: 0.1119 - dense_1_loss_25: 0.1231 - dense_1_loss_26: 0.1100 - dense_1_loss_27: 0.1122 - dense_1_loss_28: 0.1164 - dense_1_loss_29: 0.1246 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8000 - dense_1_acc_4: 0.9500 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 10.0872 - dense_1_loss_1: 3.8861 - dense_1_loss_2: 2.0084 - dense_1_loss_3: 0.8453 - dense_1_loss_4: 0.3889 - dense_1_loss_5: 0.2591 - dense_1_loss_6: 0.2051 - dense_1_loss_7: 0.1527 - dense_1_loss_8: 0.1385 - dense_1_loss_9: 0.1241 - dense_1_loss_10: 0.1007 - dense_1_loss_11: 0.1044 - dense_1_loss_12: 0.1024 - dense_1_loss_13: 0.0899 - dense_1_loss_14: 0.0920 - dense_1_loss_15: 0.1015 - dense_1_loss_16: 0.1066 - dense_1_loss_17: 0.1004 - dense_1_loss_18: 0.0979 - dense_1_loss_19: 0.0988 - dense_1_loss_20: 0.1100 - dense_1_loss_21: 0.1161 - dense_1_loss_22: 0.0996 - dense_1_loss_23: 0.1038 - dense_1_loss_24: 0.1052 - dense_1_loss_25: 0.1140 - dense_1_loss_26: 0.1045 - dense_1_loss_27: 0.1059 - dense_1_loss_28: 0.1102 - dense_1_loss_29: 0.1149 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 9.8356 - dense_1_loss_1: 3.8820 - dense_1_loss_2: 1.9787 - dense_1_loss_3: 0.8223 - dense_1_loss_4: 0.3681 - dense_1_loss_5: 0.2436 - dense_1_loss_6: 0.1937 - dense_1_loss_7: 0.1436 - dense_1_loss_8: 0.1294 - dense_1_loss_9: 0.1174 - dense_1_loss_10: 0.0954 - dense_1_loss_11: 0.0968 - dense_1_loss_12: 0.0974 - dense_1_loss_13: 0.0844 - dense_1_loss_14: 0.0876 - dense_1_loss_15: 0.0958 - dense_1_loss_16: 0.0996 - dense_1_loss_17: 0.0949 - dense_1_loss_18: 0.0919 - dense_1_loss_19: 0.0929 - dense_1_loss_20: 0.1046 - dense_1_loss_21: 0.1083 - dense_1_loss_22: 0.0939 - dense_1_loss_23: 0.0971 - dense_1_loss_24: 0.0990 - dense_1_loss_25: 0.1067 - dense_1_loss_26: 0.0988 - dense_1_loss_27: 0.0999 - dense_1_loss_28: 0.1047 - dense_1_loss_29: 0.1071 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 9.6081 - dense_1_loss_1: 3.8778 - dense_1_loss_2: 1.9485 - dense_1_loss_3: 0.8017 - dense_1_loss_4: 0.3511 - dense_1_loss_5: 0.2313 - dense_1_loss_6: 0.1839 - dense_1_loss_7: 0.1354 - dense_1_loss_8: 0.1214 - dense_1_loss_9: 0.1117 - dense_1_loss_10: 0.0897 - dense_1_loss_11: 0.0923 - dense_1_loss_12: 0.0924 - dense_1_loss_13: 0.0792 - dense_1_loss_14: 0.0828 - dense_1_loss_15: 0.0910 - dense_1_loss_16: 0.0932 - dense_1_loss_17: 0.0897 - dense_1_loss_18: 0.0860 - dense_1_loss_19: 0.0875 - dense_1_loss_20: 0.0985 - dense_1_loss_21: 0.1019 - dense_1_loss_22: 0.0886 - dense_1_loss_23: 0.0923 - dense_1_loss_24: 0.0929 - dense_1_loss_25: 0.1015 - dense_1_loss_26: 0.0922 - dense_1_loss_27: 0.0946 - dense_1_loss_28: 0.0975 - dense_1_loss_29: 0.1015 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 9.3983 - dense_1_loss_1: 3.8735 - dense_1_loss_2: 1.9195 - dense_1_loss_3: 0.7828 - dense_1_loss_4: 0.3347 - dense_1_loss_5: 0.2201 - dense_1_loss_6: 0.1744 - dense_1_loss_7: 0.1291 - dense_1_loss_8: 0.1147 - dense_1_loss_9: 0.1064 - dense_1_loss_10: 0.0842 - dense_1_loss_11: 0.0881 - dense_1_loss_12: 0.0867 - dense_1_loss_13: 0.0742 - dense_1_loss_14: 0.0772 - dense_1_loss_15: 0.0868 - dense_1_loss_16: 0.0879 - dense_1_loss_17: 0.0848 - dense_1_loss_18: 0.0808 - dense_1_loss_19: 0.0821 - dense_1_loss_20: 0.0924 - dense_1_loss_21: 0.0972 - dense_1_loss_22: 0.0836 - dense_1_loss_23: 0.0872 - dense_1_loss_24: 0.0877 - dense_1_loss_25: 0.0968 - dense_1_loss_26: 0.0866 - dense_1_loss_27: 0.0895 - dense_1_loss_28: 0.0910 - dense_1_loss_29: 0.0984 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.4833 - dense_1_acc_3: 0.8167 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 9.1982 - dense_1_loss_1: 3.8699 - dense_1_loss_2: 1.8924 - dense_1_loss_3: 0.7616 - dense_1_loss_4: 0.3200 - dense_1_loss_5: 0.2089 - dense_1_loss_6: 0.1659 - dense_1_loss_7: 0.1223 - dense_1_loss_8: 0.1092 - dense_1_loss_9: 0.1005 - dense_1_loss_10: 0.0800 - dense_1_loss_11: 0.0833 - dense_1_loss_12: 0.0822 - dense_1_loss_13: 0.0704 - dense_1_loss_14: 0.0727 - dense_1_loss_15: 0.0825 - dense_1_loss_16: 0.0834 - dense_1_loss_17: 0.0798 - dense_1_loss_18: 0.0760 - dense_1_loss_19: 0.0779 - dense_1_loss_20: 0.0869 - dense_1_loss_21: 0.0916 - dense_1_loss_22: 0.0792 - dense_1_loss_23: 0.0822 - dense_1_loss_24: 0.0829 - dense_1_loss_25: 0.0909 - dense_1_loss_26: 0.0814 - dense_1_loss_27: 0.0854 - dense_1_loss_28: 0.0868 - dense_1_loss_29: 0.0920 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9667 - dense_1_acc_5: 0.9667 - dense_1_acc_6: 0.9833 - dense_1_acc_7: 1.0000 - dense_1_acc_8: 1.0000 - dense_1_acc_9: 0.9833 - 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: 9.0141 - dense_1_loss_1: 3.8658 - dense_1_loss_2: 1.8647 - dense_1_loss_3: 0.7430 - dense_1_loss_4: 0.3070 - dense_1_loss_5: 0.1982 - dense_1_loss_6: 0.1582 - dense_1_loss_7: 0.1158 - dense_1_loss_8: 0.1039 - dense_1_loss_9: 0.0948 - dense_1_loss_10: 0.0766 - dense_1_loss_11: 0.0783 - dense_1_loss_12: 0.0784 - dense_1_loss_13: 0.0672 - dense_1_loss_14: 0.0689 - dense_1_loss_15: 0.0783 - dense_1_loss_16: 0.0791 - dense_1_loss_17: 0.0756 - dense_1_loss_18: 0.0722 - dense_1_loss_19: 0.0739 - dense_1_loss_20: 0.0827 - dense_1_loss_21: 0.0867 - dense_1_loss_22: 0.0748 - dense_1_loss_23: 0.0777 - dense_1_loss_24: 0.0785 - dense_1_loss_25: 0.0861 - dense_1_loss_26: 0.0777 - dense_1_loss_27: 0.0809 - dense_1_loss_28: 0.0828 - dense_1_loss_29: 0.0863 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - 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 61/100
60/60 [==============================] - 0s - loss: 8.8464 - dense_1_loss_1: 3.8618 - dense_1_loss_2: 1.8397 - dense_1_loss_3: 0.7255 - dense_1_loss_4: 0.2923 - dense_1_loss_5: 0.1895 - dense_1_loss_6: 0.1520 - dense_1_loss_7: 0.1102 - dense_1_loss_8: 0.0993 - dense_1_loss_9: 0.0908 - dense_1_loss_10: 0.0727 - dense_1_loss_11: 0.0744 - dense_1_loss_12: 0.0749 - dense_1_loss_13: 0.0642 - dense_1_loss_14: 0.0661 - dense_1_loss_15: 0.0740 - dense_1_loss_16: 0.0753 - dense_1_loss_17: 0.0720 - dense_1_loss_18: 0.0686 - dense_1_loss_19: 0.0702 - dense_1_loss_20: 0.0785 - dense_1_loss_21: 0.0821 - dense_1_loss_22: 0.0706 - dense_1_loss_23: 0.0741 - dense_1_loss_24: 0.0744 - dense_1_loss_25: 0.0816 - dense_1_loss_26: 0.0743 - dense_1_loss_27: 0.0770 - dense_1_loss_28: 0.0784 - dense_1_loss_29: 0.0819 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - 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 62/100
60/60 [==============================] - 0s - loss: 8.6903 - dense_1_loss_1: 3.8580 - dense_1_loss_2: 1.8134 - dense_1_loss_3: 0.7088 - dense_1_loss_4: 0.2804 - dense_1_loss_5: 0.1815 - dense_1_loss_6: 0.1450 - dense_1_loss_7: 0.1049 - dense_1_loss_8: 0.0948 - dense_1_loss_9: 0.0868 - dense_1_loss_10: 0.0690 - dense_1_loss_11: 0.0713 - dense_1_loss_12: 0.0712 - dense_1_loss_13: 0.0613 - dense_1_loss_14: 0.0632 - dense_1_loss_15: 0.0703 - dense_1_loss_16: 0.0716 - dense_1_loss_17: 0.0687 - dense_1_loss_18: 0.0657 - dense_1_loss_19: 0.0665 - dense_1_loss_20: 0.0748 - dense_1_loss_21: 0.0785 - dense_1_loss_22: 0.0673 - dense_1_loss_23: 0.0710 - dense_1_loss_24: 0.0708 - dense_1_loss_25: 0.0781 - dense_1_loss_26: 0.0709 - dense_1_loss_27: 0.0735 - dense_1_loss_28: 0.0746 - dense_1_loss_29: 0.0786 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - 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 63/100
60/60 [==============================] - 0s - loss: 8.5413 - dense_1_loss_1: 3.8545 - dense_1_loss_2: 1.7898 - dense_1_loss_3: 0.6923 - dense_1_loss_4: 0.2689 - dense_1_loss_5: 0.1728 - dense_1_loss_6: 0.1382 - dense_1_loss_7: 0.1002 - dense_1_loss_8: 0.0906 - dense_1_loss_9: 0.0828 - dense_1_loss_10: 0.0658 - dense_1_loss_11: 0.0680 - dense_1_loss_12: 0.0680 - dense_1_loss_13: 0.0584 - dense_1_loss_14: 0.0604 - dense_1_loss_15: 0.0670 - dense_1_loss_16: 0.0681 - dense_1_loss_17: 0.0655 - dense_1_loss_18: 0.0627 - dense_1_loss_19: 0.0630 - dense_1_loss_20: 0.0713 - dense_1_loss_21: 0.0750 - dense_1_loss_22: 0.0640 - dense_1_loss_23: 0.0682 - dense_1_loss_24: 0.0673 - dense_1_loss_25: 0.0747 - dense_1_loss_26: 0.0679 - dense_1_loss_27: 0.0699 - dense_1_loss_28: 0.0705 - dense_1_loss_29: 0.0758 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.8333 - dense_1_acc_4: 0.9833 - dense_1_acc_5: 1.0000 - 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 64/100
60/60 [==============================] - 0s - loss: 8.3991 - dense_1_loss_1: 3.8506 - dense_1_loss_2: 1.7670 - dense_1_loss_3: 0.6757 - dense_1_loss_4: 0.2591 - dense_1_loss_5: 0.1643 - dense_1_loss_6: 0.1314 - dense_1_loss_7: 0.0957 - dense_1_loss_8: 0.0864 - dense_1_loss_9: 0.0786 - dense_1_loss_10: 0.0637 - dense_1_loss_11: 0.0644 - dense_1_loss_12: 0.0652 - dense_1_loss_13: 0.0560 - dense_1_loss_14: 0.0575 - dense_1_loss_15: 0.0641 - dense_1_loss_16: 0.0649 - dense_1_loss_17: 0.0622 - dense_1_loss_18: 0.0601 - dense_1_loss_19: 0.0602 - dense_1_loss_20: 0.0678 - dense_1_loss_21: 0.0714 - dense_1_loss_22: 0.0613 - dense_1_loss_23: 0.0650 - dense_1_loss_24: 0.0643 - dense_1_loss_25: 0.0709 - dense_1_loss_26: 0.0647 - dense_1_loss_27: 0.0669 - dense_1_loss_28: 0.0676 - dense_1_loss_29: 0.0720 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5000 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - 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 65/100
60/60 [==============================] - 0s - loss: 8.2689 - dense_1_loss_1: 3.8471 - dense_1_loss_2: 1.7441 - dense_1_loss_3: 0.6611 - dense_1_loss_4: 0.2493 - dense_1_loss_5: 0.1574 - dense_1_loss_6: 0.1262 - dense_1_loss_7: 0.0917 - dense_1_loss_8: 0.0831 - dense_1_loss_9: 0.0750 - dense_1_loss_10: 0.0610 - dense_1_loss_11: 0.0615 - dense_1_loss_12: 0.0625 - dense_1_loss_13: 0.0536 - dense_1_loss_14: 0.0550 - dense_1_loss_15: 0.0613 - dense_1_loss_16: 0.0624 - dense_1_loss_17: 0.0592 - dense_1_loss_18: 0.0571 - dense_1_loss_19: 0.0580 - dense_1_loss_20: 0.0646 - dense_1_loss_21: 0.0680 - dense_1_loss_22: 0.0589 - dense_1_loss_23: 0.0617 - dense_1_loss_24: 0.0619 - dense_1_loss_25: 0.0674 - dense_1_loss_26: 0.0616 - dense_1_loss_27: 0.0645 - dense_1_loss_28: 0.0651 - dense_1_loss_29: 0.0684 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5167 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - 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 66/100
60/60 [==============================] - 0s - loss: 8.1463 - dense_1_loss_1: 3.8435 - dense_1_loss_2: 1.7225 - dense_1_loss_3: 0.6469 - dense_1_loss_4: 0.2401 - dense_1_loss_5: 0.1505 - dense_1_loss_6: 0.1214 - dense_1_loss_7: 0.0880 - dense_1_loss_8: 0.0800 - dense_1_loss_9: 0.0720 - dense_1_loss_10: 0.0579 - dense_1_loss_11: 0.0591 - dense_1_loss_12: 0.0599 - dense_1_loss_13: 0.0513 - dense_1_loss_14: 0.0527 - dense_1_loss_15: 0.0588 - dense_1_loss_16: 0.0599 - dense_1_loss_17: 0.0568 - dense_1_loss_18: 0.0541 - dense_1_loss_19: 0.0558 - dense_1_loss_20: 0.0618 - dense_1_loss_21: 0.0651 - dense_1_loss_22: 0.0565 - dense_1_loss_23: 0.0590 - dense_1_loss_24: 0.0593 - dense_1_loss_25: 0.0646 - dense_1_loss_26: 0.0590 - dense_1_loss_27: 0.0620 - dense_1_loss_28: 0.0623 - dense_1_loss_29: 0.0657 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - 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 67/100
60/60 [==============================] - 0s - loss: 8.0327 - dense_1_loss_1: 3.8397 - dense_1_loss_2: 1.7011 - dense_1_loss_3: 0.6337 - dense_1_loss_4: 0.2320 - dense_1_loss_5: 0.1450 - dense_1_loss_6: 0.1172 - dense_1_loss_7: 0.0846 - dense_1_loss_8: 0.0766 - dense_1_loss_9: 0.0698 - dense_1_loss_10: 0.0555 - dense_1_loss_11: 0.0570 - dense_1_loss_12: 0.0577 - dense_1_loss_13: 0.0493 - dense_1_loss_14: 0.0507 - dense_1_loss_15: 0.0564 - dense_1_loss_16: 0.0572 - dense_1_loss_17: 0.0548 - dense_1_loss_18: 0.0519 - dense_1_loss_19: 0.0532 - dense_1_loss_20: 0.0594 - dense_1_loss_21: 0.0624 - dense_1_loss_22: 0.0543 - dense_1_loss_23: 0.0566 - dense_1_loss_24: 0.0566 - dense_1_loss_25: 0.0620 - dense_1_loss_26: 0.0565 - dense_1_loss_27: 0.0592 - dense_1_loss_28: 0.0594 - dense_1_loss_29: 0.0633 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - 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 68/100
60/60 [==============================] - 0s - loss: 7.9228 - dense_1_loss_1: 3.8362 - dense_1_loss_2: 1.6800 - dense_1_loss_3: 0.6198 - dense_1_loss_4: 0.2237 - dense_1_loss_5: 0.1392 - dense_1_loss_6: 0.1122 - dense_1_loss_7: 0.0812 - dense_1_loss_8: 0.0731 - dense_1_loss_9: 0.0674 - dense_1_loss_10: 0.0536 - dense_1_loss_11: 0.0549 - dense_1_loss_12: 0.0556 - dense_1_loss_13: 0.0473 - dense_1_loss_14: 0.0488 - dense_1_loss_15: 0.0542 - dense_1_loss_16: 0.0546 - dense_1_loss_17: 0.0528 - dense_1_loss_18: 0.0499 - dense_1_loss_19: 0.0509 - dense_1_loss_20: 0.0574 - dense_1_loss_21: 0.0601 - dense_1_loss_22: 0.0523 - dense_1_loss_23: 0.0544 - dense_1_loss_24: 0.0544 - dense_1_loss_25: 0.0599 - dense_1_loss_26: 0.0540 - dense_1_loss_27: 0.0569 - dense_1_loss_28: 0.0568 - dense_1_loss_29: 0.0610 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5333 - dense_1_acc_3: 0.8500 - dense_1_acc_4: 1.0000 - dense_1_acc_5: 1.0000 - 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 69/100
60/60 [==============================] - 0s - loss: 7.8181 - dense_1_loss_1: 3.8327 - dense_1_loss_2: 1.6604 - dense_1_loss_3: 0.6072 - dense_1_loss_4: 0.2160 - dense_1_loss_5: 0.1336 - dense_1_loss_6: 0.1069 - dense_1_loss_7: 0.0779 - dense_1_loss_8: 0.0702 - dense_1_loss_9: 0.0645 - dense_1_loss_10: 0.0517 - dense_1_loss_11: 0.0525 - dense_1_loss_12: 0.0535 - dense_1_loss_13: 0.0456 - dense_1_loss_14: 0.0467 - dense_1_loss_15: 0.0525 - dense_1_loss_16: 0.0525 - dense_1_loss_17: 0.0508 - dense_1_loss_18: 0.0481 - dense_1_loss_19: 0.0486 - dense_1_loss_20: 0.0553 - dense_1_loss_21: 0.0580 - dense_1_loss_22: 0.0500 - dense_1_loss_23: 0.0524 - dense_1_loss_24: 0.0526 - dense_1_loss_25: 0.0576 - dense_1_loss_26: 0.0519 - dense_1_loss_27: 0.0551 - dense_1_loss_28: 0.0546 - dense_1_loss_29: 0.0589 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.5500 - dense_1_acc_3: 0.8833 - 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: 7.7190 - dense_1_loss_1: 3.8294 - dense_1_loss_2: 1.6399 - dense_1_loss_3: 0.5944 - dense_1_loss_4: 0.2092 - dense_1_loss_5: 0.1286 - dense_1_loss_6: 0.1027 - dense_1_loss_7: 0.0750 - dense_1_loss_8: 0.0678 - dense_1_loss_9: 0.0620 - dense_1_loss_10: 0.0496 - dense_1_loss_11: 0.0505 - dense_1_loss_12: 0.0514 - dense_1_loss_13: 0.0439 - dense_1_loss_14: 0.0449 - dense_1_loss_15: 0.0505 - dense_1_loss_16: 0.0508 - dense_1_loss_17: 0.0487 - dense_1_loss_18: 0.0463 - dense_1_loss_19: 0.0469 - dense_1_loss_20: 0.0531 - dense_1_loss_21: 0.0560 - dense_1_loss_22: 0.0482 - dense_1_loss_23: 0.0507 - dense_1_loss_24: 0.0506 - dense_1_loss_25: 0.0554 - dense_1_loss_26: 0.0502 - dense_1_loss_27: 0.0531 - dense_1_loss_28: 0.0528 - dense_1_loss_29: 0.0566 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - 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: 7.6259 - dense_1_loss_1: 3.8259 - dense_1_loss_2: 1.6217 - dense_1_loss_3: 0.5826 - dense_1_loss_4: 0.2023 - dense_1_loss_5: 0.1236 - dense_1_loss_6: 0.0989 - dense_1_loss_7: 0.0724 - dense_1_loss_8: 0.0656 - dense_1_loss_9: 0.0596 - dense_1_loss_10: 0.0477 - dense_1_loss_11: 0.0488 - dense_1_loss_12: 0.0496 - dense_1_loss_13: 0.0423 - dense_1_loss_14: 0.0434 - dense_1_loss_15: 0.0486 - dense_1_loss_16: 0.0491 - dense_1_loss_17: 0.0468 - dense_1_loss_18: 0.0446 - dense_1_loss_19: 0.0451 - dense_1_loss_20: 0.0511 - dense_1_loss_21: 0.0539 - dense_1_loss_22: 0.0465 - dense_1_loss_23: 0.0489 - dense_1_loss_24: 0.0487 - dense_1_loss_25: 0.0531 - dense_1_loss_26: 0.0484 - dense_1_loss_27: 0.0512 - dense_1_loss_28: 0.0510 - dense_1_loss_29: 0.0541 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6000 - dense_1_acc_3: 0.8833 - 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: 7.5371 - dense_1_loss_1: 3.8227 - dense_1_loss_2: 1.6023 - dense_1_loss_3: 0.5712 - dense_1_loss_4: 0.1963 - dense_1_loss_5: 0.1193 - dense_1_loss_6: 0.0952 - dense_1_loss_7: 0.0699 - dense_1_loss_8: 0.0634 - dense_1_loss_9: 0.0575 - dense_1_loss_10: 0.0461 - dense_1_loss_11: 0.0471 - dense_1_loss_12: 0.0480 - dense_1_loss_13: 0.0409 - dense_1_loss_14: 0.0421 - dense_1_loss_15: 0.0468 - dense_1_loss_16: 0.0474 - dense_1_loss_17: 0.0450 - dense_1_loss_18: 0.0430 - dense_1_loss_19: 0.0436 - dense_1_loss_20: 0.0493 - dense_1_loss_21: 0.0519 - dense_1_loss_22: 0.0450 - dense_1_loss_23: 0.0473 - dense_1_loss_24: 0.0470 - dense_1_loss_25: 0.0512 - dense_1_loss_26: 0.0466 - dense_1_loss_27: 0.0495 - dense_1_loss_28: 0.0492 - dense_1_loss_29: 0.0522 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.4548 - dense_1_loss_1: 3.8191 - dense_1_loss_2: 1.5848 - dense_1_loss_3: 0.5607 - dense_1_loss_4: 0.1901 - dense_1_loss_5: 0.1154 - dense_1_loss_6: 0.0919 - dense_1_loss_7: 0.0676 - dense_1_loss_8: 0.0614 - dense_1_loss_9: 0.0555 - dense_1_loss_10: 0.0447 - dense_1_loss_11: 0.0455 - dense_1_loss_12: 0.0465 - dense_1_loss_13: 0.0395 - dense_1_loss_14: 0.0408 - dense_1_loss_15: 0.0453 - dense_1_loss_16: 0.0458 - dense_1_loss_17: 0.0435 - dense_1_loss_18: 0.0415 - dense_1_loss_19: 0.0421 - dense_1_loss_20: 0.0476 - dense_1_loss_21: 0.0501 - dense_1_loss_22: 0.0434 - dense_1_loss_23: 0.0458 - dense_1_loss_24: 0.0454 - dense_1_loss_25: 0.0496 - dense_1_loss_26: 0.0451 - dense_1_loss_27: 0.0478 - dense_1_loss_28: 0.0474 - dense_1_loss_29: 0.0506 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.3717 - dense_1_loss_1: 3.8157 - dense_1_loss_2: 1.5671 - dense_1_loss_3: 0.5497 - dense_1_loss_4: 0.1840 - dense_1_loss_5: 0.1114 - dense_1_loss_6: 0.0877 - dense_1_loss_7: 0.0653 - dense_1_loss_8: 0.0592 - dense_1_loss_9: 0.0533 - dense_1_loss_10: 0.0432 - dense_1_loss_11: 0.0439 - dense_1_loss_12: 0.0450 - dense_1_loss_13: 0.0382 - dense_1_loss_14: 0.0393 - dense_1_loss_15: 0.0440 - dense_1_loss_16: 0.0441 - dense_1_loss_17: 0.0421 - dense_1_loss_18: 0.0401 - dense_1_loss_19: 0.0407 - dense_1_loss_20: 0.0461 - dense_1_loss_21: 0.0485 - dense_1_loss_22: 0.0419 - dense_1_loss_23: 0.0443 - dense_1_loss_24: 0.0439 - dense_1_loss_25: 0.0482 - dense_1_loss_26: 0.0438 - dense_1_loss_27: 0.0460 - dense_1_loss_28: 0.0457 - dense_1_loss_29: 0.0493 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.2966 - dense_1_loss_1: 3.8127 - dense_1_loss_2: 1.5499 - dense_1_loss_3: 0.5389 - dense_1_loss_4: 0.1789 - dense_1_loss_5: 0.1077 - dense_1_loss_6: 0.0847 - dense_1_loss_7: 0.0634 - dense_1_loss_8: 0.0574 - dense_1_loss_9: 0.0515 - dense_1_loss_10: 0.0419 - dense_1_loss_11: 0.0425 - dense_1_loss_12: 0.0436 - dense_1_loss_13: 0.0368 - dense_1_loss_14: 0.0381 - dense_1_loss_15: 0.0427 - dense_1_loss_16: 0.0426 - dense_1_loss_17: 0.0409 - dense_1_loss_18: 0.0388 - dense_1_loss_19: 0.0394 - dense_1_loss_20: 0.0447 - dense_1_loss_21: 0.0471 - dense_1_loss_22: 0.0407 - dense_1_loss_23: 0.0428 - dense_1_loss_24: 0.0426 - dense_1_loss_25: 0.0467 - dense_1_loss_26: 0.0426 - dense_1_loss_27: 0.0446 - dense_1_loss_28: 0.0444 - dense_1_loss_29: 0.0480 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.2225 - dense_1_loss_1: 3.8095 - dense_1_loss_2: 1.5334 - dense_1_loss_3: 0.5285 - dense_1_loss_4: 0.1741 - dense_1_loss_5: 0.1043 - dense_1_loss_6: 0.0817 - dense_1_loss_7: 0.0613 - dense_1_loss_8: 0.0554 - dense_1_loss_9: 0.0499 - dense_1_loss_10: 0.0407 - dense_1_loss_11: 0.0411 - dense_1_loss_12: 0.0423 - dense_1_loss_13: 0.0358 - dense_1_loss_14: 0.0369 - dense_1_loss_15: 0.0414 - dense_1_loss_16: 0.0413 - dense_1_loss_17: 0.0396 - dense_1_loss_18: 0.0374 - dense_1_loss_19: 0.0381 - dense_1_loss_20: 0.0434 - dense_1_loss_21: 0.0454 - dense_1_loss_22: 0.0395 - dense_1_loss_23: 0.0413 - dense_1_loss_24: 0.0413 - dense_1_loss_25: 0.0452 - dense_1_loss_26: 0.0411 - dense_1_loss_27: 0.0433 - dense_1_loss_28: 0.0431 - dense_1_loss_29: 0.0462 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.1549 - dense_1_loss_1: 3.8063 - dense_1_loss_2: 1.5175 - dense_1_loss_3: 0.5189 - dense_1_loss_4: 0.1696 - dense_1_loss_5: 0.1013 - dense_1_loss_6: 0.0794 - dense_1_loss_7: 0.0595 - dense_1_loss_8: 0.0539 - dense_1_loss_9: 0.0484 - dense_1_loss_10: 0.0396 - dense_1_loss_11: 0.0399 - dense_1_loss_12: 0.0412 - dense_1_loss_13: 0.0347 - dense_1_loss_14: 0.0359 - dense_1_loss_15: 0.0402 - dense_1_loss_16: 0.0400 - dense_1_loss_17: 0.0384 - dense_1_loss_18: 0.0363 - dense_1_loss_19: 0.0370 - dense_1_loss_20: 0.0422 - dense_1_loss_21: 0.0440 - dense_1_loss_22: 0.0383 - dense_1_loss_23: 0.0401 - dense_1_loss_24: 0.0401 - dense_1_loss_25: 0.0437 - dense_1_loss_26: 0.0400 - dense_1_loss_27: 0.0420 - dense_1_loss_28: 0.0418 - dense_1_loss_29: 0.0448 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.0869 - dense_1_loss_1: 3.8030 - dense_1_loss_2: 1.5015 - dense_1_loss_3: 0.5094 - dense_1_loss_4: 0.1650 - dense_1_loss_5: 0.0981 - dense_1_loss_6: 0.0764 - dense_1_loss_7: 0.0577 - dense_1_loss_8: 0.0522 - dense_1_loss_9: 0.0469 - dense_1_loss_10: 0.0384 - dense_1_loss_11: 0.0388 - dense_1_loss_12: 0.0399 - dense_1_loss_13: 0.0337 - dense_1_loss_14: 0.0348 - dense_1_loss_15: 0.0390 - dense_1_loss_16: 0.0388 - dense_1_loss_17: 0.0372 - dense_1_loss_18: 0.0351 - dense_1_loss_19: 0.0360 - dense_1_loss_20: 0.0409 - dense_1_loss_21: 0.0428 - dense_1_loss_22: 0.0372 - dense_1_loss_23: 0.0389 - dense_1_loss_24: 0.0389 - dense_1_loss_25: 0.0425 - dense_1_loss_26: 0.0388 - dense_1_loss_27: 0.0409 - dense_1_loss_28: 0.0405 - dense_1_loss_29: 0.0435 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 7.0205 - dense_1_loss_1: 3.8002 - dense_1_loss_2: 1.4861 - dense_1_loss_3: 0.4989 - dense_1_loss_4: 0.1606 - dense_1_loss_5: 0.0950 - dense_1_loss_6: 0.0734 - dense_1_loss_7: 0.0559 - dense_1_loss_8: 0.0507 - dense_1_loss_9: 0.0454 - dense_1_loss_10: 0.0373 - dense_1_loss_11: 0.0377 - dense_1_loss_12: 0.0388 - dense_1_loss_13: 0.0328 - dense_1_loss_14: 0.0338 - dense_1_loss_15: 0.0379 - dense_1_loss_16: 0.0377 - dense_1_loss_17: 0.0361 - dense_1_loss_18: 0.0341 - dense_1_loss_19: 0.0349 - dense_1_loss_20: 0.0396 - dense_1_loss_21: 0.0417 - dense_1_loss_22: 0.0361 - dense_1_loss_23: 0.0379 - dense_1_loss_24: 0.0378 - dense_1_loss_25: 0.0412 - dense_1_loss_26: 0.0377 - dense_1_loss_27: 0.0397 - dense_1_loss_28: 0.0393 - dense_1_loss_29: 0.0424 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.9591 - dense_1_loss_1: 3.7969 - dense_1_loss_2: 1.4708 - dense_1_loss_3: 0.4904 - dense_1_loss_4: 0.1566 - dense_1_loss_5: 0.0923 - dense_1_loss_6: 0.0713 - dense_1_loss_7: 0.0543 - dense_1_loss_8: 0.0493 - dense_1_loss_9: 0.0442 - dense_1_loss_10: 0.0362 - dense_1_loss_11: 0.0366 - dense_1_loss_12: 0.0376 - dense_1_loss_13: 0.0319 - dense_1_loss_14: 0.0328 - dense_1_loss_15: 0.0369 - dense_1_loss_16: 0.0366 - dense_1_loss_17: 0.0350 - dense_1_loss_18: 0.0332 - dense_1_loss_19: 0.0339 - dense_1_loss_20: 0.0384 - dense_1_loss_21: 0.0405 - dense_1_loss_22: 0.0352 - dense_1_loss_23: 0.0369 - dense_1_loss_24: 0.0367 - dense_1_loss_25: 0.0401 - dense_1_loss_26: 0.0366 - dense_1_loss_27: 0.0387 - dense_1_loss_28: 0.0381 - dense_1_loss_29: 0.0412 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.8991 - dense_1_loss_1: 3.7938 - dense_1_loss_2: 1.4561 - dense_1_loss_3: 0.4811 - dense_1_loss_4: 0.1529 - dense_1_loss_5: 0.0896 - dense_1_loss_6: 0.0688 - dense_1_loss_7: 0.0528 - dense_1_loss_8: 0.0477 - dense_1_loss_9: 0.0428 - dense_1_loss_10: 0.0352 - dense_1_loss_11: 0.0355 - dense_1_loss_12: 0.0367 - dense_1_loss_13: 0.0310 - dense_1_loss_14: 0.0319 - dense_1_loss_15: 0.0358 - dense_1_loss_16: 0.0355 - dense_1_loss_17: 0.0341 - dense_1_loss_18: 0.0322 - dense_1_loss_19: 0.0330 - dense_1_loss_20: 0.0374 - dense_1_loss_21: 0.0394 - dense_1_loss_22: 0.0343 - dense_1_loss_23: 0.0359 - dense_1_loss_24: 0.0358 - dense_1_loss_25: 0.0391 - dense_1_loss_26: 0.0356 - dense_1_loss_27: 0.0378 - dense_1_loss_28: 0.0372 - dense_1_loss_29: 0.0401 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.8436 - dense_1_loss_1: 3.7907 - dense_1_loss_2: 1.4420 - dense_1_loss_3: 0.4731 - dense_1_loss_4: 0.1494 - dense_1_loss_5: 0.0874 - dense_1_loss_6: 0.0671 - dense_1_loss_7: 0.0514 - dense_1_loss_8: 0.0466 - dense_1_loss_9: 0.0417 - dense_1_loss_10: 0.0342 - dense_1_loss_11: 0.0345 - dense_1_loss_12: 0.0358 - dense_1_loss_13: 0.0302 - dense_1_loss_14: 0.0311 - dense_1_loss_15: 0.0350 - dense_1_loss_16: 0.0346 - dense_1_loss_17: 0.0332 - dense_1_loss_18: 0.0313 - dense_1_loss_19: 0.0321 - dense_1_loss_20: 0.0364 - dense_1_loss_21: 0.0383 - dense_1_loss_22: 0.0334 - dense_1_loss_23: 0.0349 - dense_1_loss_24: 0.0348 - dense_1_loss_25: 0.0379 - dense_1_loss_26: 0.0346 - dense_1_loss_27: 0.0368 - dense_1_loss_28: 0.0362 - dense_1_loss_29: 0.0388 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.7859 - dense_1_loss_1: 3.7877 - dense_1_loss_2: 1.4271 - dense_1_loss_3: 0.4642 - dense_1_loss_4: 0.1457 - dense_1_loss_5: 0.0850 - dense_1_loss_6: 0.0648 - dense_1_loss_7: 0.0499 - dense_1_loss_8: 0.0451 - dense_1_loss_9: 0.0405 - dense_1_loss_10: 0.0332 - dense_1_loss_11: 0.0336 - dense_1_loss_12: 0.0348 - dense_1_loss_13: 0.0294 - dense_1_loss_14: 0.0303 - dense_1_loss_15: 0.0340 - dense_1_loss_16: 0.0337 - dense_1_loss_17: 0.0323 - dense_1_loss_18: 0.0304 - dense_1_loss_19: 0.0313 - dense_1_loss_20: 0.0355 - dense_1_loss_21: 0.0372 - dense_1_loss_22: 0.0325 - dense_1_loss_23: 0.0340 - dense_1_loss_24: 0.0339 - dense_1_loss_25: 0.0369 - dense_1_loss_26: 0.0338 - dense_1_loss_27: 0.0358 - dense_1_loss_28: 0.0353 - dense_1_loss_29: 0.0378 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.7328 - dense_1_loss_1: 3.7848 - dense_1_loss_2: 1.4126 - dense_1_loss_3: 0.4564 - dense_1_loss_4: 0.1424 - dense_1_loss_5: 0.0829 - dense_1_loss_6: 0.0628 - dense_1_loss_7: 0.0487 - dense_1_loss_8: 0.0441 - dense_1_loss_9: 0.0395 - dense_1_loss_10: 0.0323 - dense_1_loss_11: 0.0329 - dense_1_loss_12: 0.0338 - dense_1_loss_13: 0.0287 - dense_1_loss_14: 0.0295 - dense_1_loss_15: 0.0332 - dense_1_loss_16: 0.0329 - dense_1_loss_17: 0.0314 - dense_1_loss_18: 0.0297 - dense_1_loss_19: 0.0305 - dense_1_loss_20: 0.0346 - dense_1_loss_21: 0.0363 - dense_1_loss_22: 0.0317 - dense_1_loss_23: 0.0332 - dense_1_loss_24: 0.0330 - dense_1_loss_25: 0.0360 - dense_1_loss_26: 0.0329 - dense_1_loss_27: 0.0349 - dense_1_loss_28: 0.0343 - dense_1_loss_29: 0.0370 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.6812 - dense_1_loss_1: 3.7817 - dense_1_loss_2: 1.3998 - dense_1_loss_3: 0.4478 - dense_1_loss_4: 0.1391 - dense_1_loss_5: 0.0806 - dense_1_loss_6: 0.0610 - dense_1_loss_7: 0.0474 - dense_1_loss_8: 0.0429 - dense_1_loss_9: 0.0384 - dense_1_loss_10: 0.0315 - dense_1_loss_11: 0.0320 - dense_1_loss_12: 0.0330 - dense_1_loss_13: 0.0280 - dense_1_loss_14: 0.0288 - dense_1_loss_15: 0.0324 - dense_1_loss_16: 0.0321 - dense_1_loss_17: 0.0305 - dense_1_loss_18: 0.0290 - dense_1_loss_19: 0.0297 - dense_1_loss_20: 0.0338 - dense_1_loss_21: 0.0354 - dense_1_loss_22: 0.0309 - dense_1_loss_23: 0.0324 - dense_1_loss_24: 0.0322 - dense_1_loss_25: 0.0352 - dense_1_loss_26: 0.0321 - dense_1_loss_27: 0.0339 - dense_1_loss_28: 0.0334 - dense_1_loss_29: 0.0361 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - dense_1_acc_3: 0.8833 - 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: 6.6320 - dense_1_loss_1: 3.7790 - dense_1_loss_2: 1.3860 - dense_1_loss_3: 0.4403 - dense_1_loss_4: 0.1362 - dense_1_loss_5: 0.0788 - dense_1_loss_6: 0.0593 - dense_1_loss_7: 0.0463 - dense_1_loss_8: 0.0420 - dense_1_loss_9: 0.0374 - dense_1_loss_10: 0.0307 - dense_1_loss_11: 0.0313 - dense_1_loss_12: 0.0322 - dense_1_loss_13: 0.0272 - dense_1_loss_14: 0.0281 - dense_1_loss_15: 0.0316 - dense_1_loss_16: 0.0313 - dense_1_loss_17: 0.0297 - dense_1_loss_18: 0.0282 - dense_1_loss_19: 0.0289 - dense_1_loss_20: 0.0329 - dense_1_loss_21: 0.0346 - dense_1_loss_22: 0.0302 - dense_1_loss_23: 0.0316 - dense_1_loss_24: 0.0314 - dense_1_loss_25: 0.0344 - dense_1_loss_26: 0.0313 - dense_1_loss_27: 0.0331 - dense_1_loss_28: 0.0326 - dense_1_loss_29: 0.0353 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 87/100
60/60 [==============================] - 0s - loss: 6.5850 - dense_1_loss_1: 3.7758 - dense_1_loss_2: 1.3737 - dense_1_loss_3: 0.4333 - dense_1_loss_4: 0.1332 - dense_1_loss_5: 0.0770 - dense_1_loss_6: 0.0578 - dense_1_loss_7: 0.0452 - dense_1_loss_8: 0.0410 - dense_1_loss_9: 0.0365 - dense_1_loss_10: 0.0300 - dense_1_loss_11: 0.0305 - dense_1_loss_12: 0.0314 - dense_1_loss_13: 0.0265 - dense_1_loss_14: 0.0274 - dense_1_loss_15: 0.0309 - dense_1_loss_16: 0.0305 - dense_1_loss_17: 0.0290 - dense_1_loss_18: 0.0275 - dense_1_loss_19: 0.0282 - dense_1_loss_20: 0.0321 - dense_1_loss_21: 0.0337 - dense_1_loss_22: 0.0295 - dense_1_loss_23: 0.0309 - dense_1_loss_24: 0.0307 - dense_1_loss_25: 0.0336 - dense_1_loss_26: 0.0305 - dense_1_loss_27: 0.0323 - dense_1_loss_28: 0.0319 - dense_1_loss_29: 0.0343 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 88/100
60/60 [==============================] - 0s - loss: 6.5377 - dense_1_loss_1: 3.7728 - dense_1_loss_2: 1.3607 - dense_1_loss_3: 0.4255 - dense_1_loss_4: 0.1306 - dense_1_loss_5: 0.0751 - dense_1_loss_6: 0.0563 - dense_1_loss_7: 0.0441 - dense_1_loss_8: 0.0400 - dense_1_loss_9: 0.0356 - dense_1_loss_10: 0.0293 - dense_1_loss_11: 0.0297 - dense_1_loss_12: 0.0307 - dense_1_loss_13: 0.0259 - dense_1_loss_14: 0.0268 - dense_1_loss_15: 0.0302 - dense_1_loss_16: 0.0296 - dense_1_loss_17: 0.0283 - dense_1_loss_18: 0.0268 - dense_1_loss_19: 0.0276 - dense_1_loss_20: 0.0314 - dense_1_loss_21: 0.0328 - dense_1_loss_22: 0.0287 - dense_1_loss_23: 0.0302 - dense_1_loss_24: 0.0300 - dense_1_loss_25: 0.0328 - dense_1_loss_26: 0.0298 - dense_1_loss_27: 0.0317 - dense_1_loss_28: 0.0312 - dense_1_loss_29: 0.0334 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 89/100
60/60 [==============================] - 0s - loss: 6.4915 - dense_1_loss_1: 3.7698 - dense_1_loss_2: 1.3477 - dense_1_loss_3: 0.4182 - dense_1_loss_4: 0.1276 - dense_1_loss_5: 0.0733 - dense_1_loss_6: 0.0546 - dense_1_loss_7: 0.0430 - dense_1_loss_8: 0.0390 - dense_1_loss_9: 0.0347 - dense_1_loss_10: 0.0285 - dense_1_loss_11: 0.0291 - dense_1_loss_12: 0.0300 - dense_1_loss_13: 0.0253 - dense_1_loss_14: 0.0262 - dense_1_loss_15: 0.0295 - dense_1_loss_16: 0.0289 - dense_1_loss_17: 0.0277 - dense_1_loss_18: 0.0261 - dense_1_loss_19: 0.0269 - dense_1_loss_20: 0.0306 - dense_1_loss_21: 0.0321 - dense_1_loss_22: 0.0281 - dense_1_loss_23: 0.0295 - dense_1_loss_24: 0.0293 - dense_1_loss_25: 0.0321 - dense_1_loss_26: 0.0292 - dense_1_loss_27: 0.0310 - dense_1_loss_28: 0.0306 - dense_1_loss_29: 0.0327 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 90/100
60/60 [==============================] - 0s - loss: 6.4485 - dense_1_loss_1: 3.7669 - dense_1_loss_2: 1.3359 - dense_1_loss_3: 0.4113 - dense_1_loss_4: 0.1248 - dense_1_loss_5: 0.0718 - dense_1_loss_6: 0.0533 - dense_1_loss_7: 0.0421 - dense_1_loss_8: 0.0382 - dense_1_loss_9: 0.0339 - dense_1_loss_10: 0.0278 - dense_1_loss_11: 0.0285 - dense_1_loss_12: 0.0293 - dense_1_loss_13: 0.0248 - dense_1_loss_14: 0.0256 - dense_1_loss_15: 0.0288 - dense_1_loss_16: 0.0283 - dense_1_loss_17: 0.0271 - dense_1_loss_18: 0.0255 - dense_1_loss_19: 0.0263 - dense_1_loss_20: 0.0300 - dense_1_loss_21: 0.0314 - dense_1_loss_22: 0.0275 - dense_1_loss_23: 0.0288 - dense_1_loss_24: 0.0286 - dense_1_loss_25: 0.0313 - dense_1_loss_26: 0.0286 - dense_1_loss_27: 0.0302 - dense_1_loss_28: 0.0299 - dense_1_loss_29: 0.0320 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 91/100
60/60 [==============================] - 0s - loss: 6.4068 - dense_1_loss_1: 3.7643 - dense_1_loss_2: 1.3239 - dense_1_loss_3: 0.4051 - dense_1_loss_4: 0.1223 - dense_1_loss_5: 0.0702 - dense_1_loss_6: 0.0519 - dense_1_loss_7: 0.0412 - dense_1_loss_8: 0.0373 - dense_1_loss_9: 0.0331 - dense_1_loss_10: 0.0272 - dense_1_loss_11: 0.0279 - dense_1_loss_12: 0.0286 - dense_1_loss_13: 0.0242 - dense_1_loss_14: 0.0250 - dense_1_loss_15: 0.0282 - dense_1_loss_16: 0.0277 - dense_1_loss_17: 0.0265 - dense_1_loss_18: 0.0249 - dense_1_loss_19: 0.0257 - dense_1_loss_20: 0.0293 - dense_1_loss_21: 0.0306 - dense_1_loss_22: 0.0269 - dense_1_loss_23: 0.0282 - dense_1_loss_24: 0.0279 - dense_1_loss_25: 0.0306 - dense_1_loss_26: 0.0280 - dense_1_loss_27: 0.0295 - dense_1_loss_28: 0.0292 - dense_1_loss_29: 0.0313 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 92/100
60/60 [==============================] - 0s - loss: 6.3647 - dense_1_loss_1: 3.7612 - dense_1_loss_2: 1.3121 - dense_1_loss_3: 0.3982 - dense_1_loss_4: 0.1196 - dense_1_loss_5: 0.0687 - dense_1_loss_6: 0.0505 - dense_1_loss_7: 0.0403 - dense_1_loss_8: 0.0365 - dense_1_loss_9: 0.0323 - dense_1_loss_10: 0.0266 - dense_1_loss_11: 0.0272 - dense_1_loss_12: 0.0280 - dense_1_loss_13: 0.0236 - dense_1_loss_14: 0.0245 - dense_1_loss_15: 0.0277 - dense_1_loss_16: 0.0271 - dense_1_loss_17: 0.0259 - dense_1_loss_18: 0.0244 - dense_1_loss_19: 0.0251 - dense_1_loss_20: 0.0287 - dense_1_loss_21: 0.0299 - dense_1_loss_22: 0.0264 - dense_1_loss_23: 0.0276 - dense_1_loss_24: 0.0273 - dense_1_loss_25: 0.0299 - dense_1_loss_26: 0.0274 - dense_1_loss_27: 0.0290 - dense_1_loss_28: 0.0285 - dense_1_loss_29: 0.0306 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 93/100
60/60 [==============================] - 0s - loss: 6.3257 - dense_1_loss_1: 3.7583 - dense_1_loss_2: 1.3010 - dense_1_loss_3: 0.3918 - dense_1_loss_4: 0.1174 - dense_1_loss_5: 0.0673 - dense_1_loss_6: 0.0493 - dense_1_loss_7: 0.0394 - dense_1_loss_8: 0.0357 - dense_1_loss_9: 0.0316 - dense_1_loss_10: 0.0261 - dense_1_loss_11: 0.0266 - dense_1_loss_12: 0.0274 - dense_1_loss_13: 0.0231 - dense_1_loss_14: 0.0239 - dense_1_loss_15: 0.0271 - dense_1_loss_16: 0.0264 - dense_1_loss_17: 0.0253 - dense_1_loss_18: 0.0239 - dense_1_loss_19: 0.0246 - dense_1_loss_20: 0.0281 - dense_1_loss_21: 0.0293 - dense_1_loss_22: 0.0259 - dense_1_loss_23: 0.0270 - dense_1_loss_24: 0.0267 - dense_1_loss_25: 0.0292 - dense_1_loss_26: 0.0268 - dense_1_loss_27: 0.0284 - dense_1_loss_28: 0.0280 - dense_1_loss_29: 0.0300 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 94/100
60/60 [==============================] - 0s - loss: 6.2875 - dense_1_loss_1: 3.7558 - dense_1_loss_2: 1.2896 - dense_1_loss_3: 0.3853 - dense_1_loss_4: 0.1153 - dense_1_loss_5: 0.0659 - dense_1_loss_6: 0.0482 - dense_1_loss_7: 0.0386 - dense_1_loss_8: 0.0350 - dense_1_loss_9: 0.0309 - dense_1_loss_10: 0.0256 - dense_1_loss_11: 0.0260 - dense_1_loss_12: 0.0268 - dense_1_loss_13: 0.0227 - dense_1_loss_14: 0.0234 - dense_1_loss_15: 0.0266 - dense_1_loss_16: 0.0259 - dense_1_loss_17: 0.0248 - dense_1_loss_18: 0.0234 - dense_1_loss_19: 0.0241 - dense_1_loss_20: 0.0275 - dense_1_loss_21: 0.0287 - dense_1_loss_22: 0.0254 - dense_1_loss_23: 0.0264 - dense_1_loss_24: 0.0262 - dense_1_loss_25: 0.0286 - dense_1_loss_26: 0.0262 - dense_1_loss_27: 0.0279 - dense_1_loss_28: 0.0274 - dense_1_loss_29: 0.0293 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 95/100
60/60 [==============================] - 0s - loss: 6.2491 - dense_1_loss_1: 3.7529 - dense_1_loss_2: 1.2789 - dense_1_loss_3: 0.3782 - dense_1_loss_4: 0.1131 - dense_1_loss_5: 0.0646 - dense_1_loss_6: 0.0470 - dense_1_loss_7: 0.0378 - dense_1_loss_8: 0.0343 - dense_1_loss_9: 0.0302 - dense_1_loss_10: 0.0251 - dense_1_loss_11: 0.0254 - dense_1_loss_12: 0.0263 - dense_1_loss_13: 0.0222 - dense_1_loss_14: 0.0229 - dense_1_loss_15: 0.0260 - dense_1_loss_16: 0.0254 - dense_1_loss_17: 0.0242 - dense_1_loss_18: 0.0229 - dense_1_loss_19: 0.0236 - dense_1_loss_20: 0.0269 - dense_1_loss_21: 0.0281 - dense_1_loss_22: 0.0249 - dense_1_loss_23: 0.0259 - dense_1_loss_24: 0.0257 - dense_1_loss_25: 0.0280 - dense_1_loss_26: 0.0257 - dense_1_loss_27: 0.0274 - dense_1_loss_28: 0.0269 - dense_1_loss_29: 0.0287 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 96/100
60/60 [==============================] - 0s - loss: 6.2130 - dense_1_loss_1: 3.7501 - dense_1_loss_2: 1.2677 - dense_1_loss_3: 0.3724 - dense_1_loss_4: 0.1111 - dense_1_loss_5: 0.0634 - dense_1_loss_6: 0.0461 - dense_1_loss_7: 0.0371 - dense_1_loss_8: 0.0336 - dense_1_loss_9: 0.0297 - dense_1_loss_10: 0.0245 - dense_1_loss_11: 0.0250 - dense_1_loss_12: 0.0257 - dense_1_loss_13: 0.0218 - dense_1_loss_14: 0.0225 - dense_1_loss_15: 0.0255 - dense_1_loss_16: 0.0249 - dense_1_loss_17: 0.0237 - dense_1_loss_18: 0.0224 - dense_1_loss_19: 0.0231 - dense_1_loss_20: 0.0263 - dense_1_loss_21: 0.0275 - dense_1_loss_22: 0.0243 - dense_1_loss_23: 0.0254 - dense_1_loss_24: 0.0252 - dense_1_loss_25: 0.0274 - dense_1_loss_26: 0.0251 - dense_1_loss_27: 0.0268 - dense_1_loss_28: 0.0263 - dense_1_loss_29: 0.0282 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 97/100
60/60 [==============================] - 0s - loss: 6.1763 - dense_1_loss_1: 3.7474 - dense_1_loss_2: 1.2566 - dense_1_loss_3: 0.3662 - dense_1_loss_4: 0.1090 - dense_1_loss_5: 0.0621 - dense_1_loss_6: 0.0450 - dense_1_loss_7: 0.0363 - dense_1_loss_8: 0.0329 - dense_1_loss_9: 0.0291 - dense_1_loss_10: 0.0240 - dense_1_loss_11: 0.0245 - dense_1_loss_12: 0.0252 - dense_1_loss_13: 0.0213 - dense_1_loss_14: 0.0221 - dense_1_loss_15: 0.0249 - dense_1_loss_16: 0.0244 - dense_1_loss_17: 0.0233 - dense_1_loss_18: 0.0219 - dense_1_loss_19: 0.0226 - dense_1_loss_20: 0.0257 - dense_1_loss_21: 0.0270 - dense_1_loss_22: 0.0239 - dense_1_loss_23: 0.0249 - dense_1_loss_24: 0.0247 - dense_1_loss_25: 0.0268 - dense_1_loss_26: 0.0246 - dense_1_loss_27: 0.0263 - dense_1_loss_28: 0.0258 - dense_1_loss_29: 0.0276 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 98/100
60/60 [==============================] - 0s - loss: 6.1431 - dense_1_loss_1: 3.7446 - dense_1_loss_2: 1.2469 - dense_1_loss_3: 0.3609 - dense_1_loss_4: 0.1070 - dense_1_loss_5: 0.0609 - dense_1_loss_6: 0.0442 - dense_1_loss_7: 0.0356 - dense_1_loss_8: 0.0323 - dense_1_loss_9: 0.0286 - dense_1_loss_10: 0.0235 - dense_1_loss_11: 0.0240 - dense_1_loss_12: 0.0247 - dense_1_loss_13: 0.0209 - dense_1_loss_14: 0.0218 - dense_1_loss_15: 0.0244 - dense_1_loss_16: 0.0239 - dense_1_loss_17: 0.0228 - dense_1_loss_18: 0.0215 - dense_1_loss_19: 0.0222 - dense_1_loss_20: 0.0253 - dense_1_loss_21: 0.0264 - dense_1_loss_22: 0.0235 - dense_1_loss_23: 0.0245 - dense_1_loss_24: 0.0242 - dense_1_loss_25: 0.0263 - dense_1_loss_26: 0.0242 - dense_1_loss_27: 0.0257 - dense_1_loss_28: 0.0253 - dense_1_loss_29: 0.0271 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 99/100
60/60 [==============================] - 0s - loss: 6.1098 - dense_1_loss_1: 3.7418 - dense_1_loss_2: 1.2364 - dense_1_loss_3: 0.3555 - dense_1_loss_4: 0.1052 - dense_1_loss_5: 0.0598 - dense_1_loss_6: 0.0433 - dense_1_loss_7: 0.0349 - dense_1_loss_8: 0.0317 - dense_1_loss_9: 0.0280 - dense_1_loss_10: 0.0231 - dense_1_loss_11: 0.0235 - dense_1_loss_12: 0.0243 - dense_1_loss_13: 0.0205 - dense_1_loss_14: 0.0213 - dense_1_loss_15: 0.0240 - dense_1_loss_16: 0.0234 - dense_1_loss_17: 0.0224 - dense_1_loss_18: 0.0210 - dense_1_loss_19: 0.0217 - dense_1_loss_20: 0.0248 - dense_1_loss_21: 0.0259 - dense_1_loss_22: 0.0230 - dense_1_loss_23: 0.0240 - dense_1_loss_24: 0.0237 - dense_1_loss_25: 0.0258 - dense_1_loss_26: 0.0238 - dense_1_loss_27: 0.0252 - dense_1_loss_28: 0.0248 - dense_1_loss_29: 0.0265 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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 100/100
60/60 [==============================] - 0s - loss: 6.0769 - dense_1_loss_1: 3.7392 - dense_1_loss_2: 1.2264 - dense_1_loss_3: 0.3498 - dense_1_loss_4: 0.1034 - dense_1_loss_5: 0.0588 - dense_1_loss_6: 0.0424 - dense_1_loss_7: 0.0342 - dense_1_loss_8: 0.0311 - dense_1_loss_9: 0.0275 - dense_1_loss_10: 0.0227 - dense_1_loss_11: 0.0230 - dense_1_loss_12: 0.0239 - dense_1_loss_13: 0.0201 - dense_1_loss_14: 0.0209 - dense_1_loss_15: 0.0236 - dense_1_loss_16: 0.0230 - dense_1_loss_17: 0.0220 - dense_1_loss_18: 0.0206 - dense_1_loss_19: 0.0213 - dense_1_loss_20: 0.0244 - dense_1_loss_21: 0.0254 - dense_1_loss_22: 0.0226 - dense_1_loss_23: 0.0236 - dense_1_loss_24: 0.0233 - dense_1_loss_25: 0.0253 - dense_1_loss_26: 0.0233 - dense_1_loss_27: 0.0247 - dense_1_loss_28: 0.0244 - dense_1_loss_29: 0.0260 - dense_1_loss_30: 0.0000e+00 - dense_1_acc_1: 0.1000 - dense_1_acc_2: 0.6333 - 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     






    Out[10]:





<keras.callbacks.History at 0x7ff03dd0e8d0>

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.]



In [11]:

    
# 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(inputs=[x0, a0, c0], outputs=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.



In [12]:

    
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.



In [13]:

    
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:

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)
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.
Convert the indices into their one-hot vector representations. Hint.



In [14]:

    
# 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, axis=-1)
    # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )
    results = to_categorical(indices, num_classes=78)
    ### END CODE HERE ###
    
    return results, indices



In [15]:

    
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]) = 5
np.argmax(results[17]) = 18
list(indices[12:18]) = [array([5]), array([18]), array([75]), array([76]), array([5]), array([18])]

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.



In [16]:

    
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 51 sounds using the predicted values for the set of chords ("2") and after pruning
Generated 50 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
Generated 51 sounds using the predicted values for the set of chords ("5") and after pruning
Your generated music is saved in output/my_music.midi

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.



In [17]:

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









    Out[17]:

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.

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])]