python - How to optimize a model using the functional api of Keras

Question

I am trying to build a model using the functional api of Keras. Here is the entire model that I have made. I am not sure if it is correct, and I would be very happy if someone could take a look at it for a moment.

I have first splittet the data into train and test data set.

from sklearn.model_selection import train_test_split

X1_train, X1_test, X2_train, X2_test, y_train, y_test = train_test_split(X1_scaled, X2_scaled, end_y, test_size=0.2)

[i.shape for i in (X1_train, X1_test, X2_train, X2_test, y_train, y_test)]

Here is the part, where I start to build the model

from tensorflow.keras import layers, Model, utils


# Build the model
input1 = layers.Input((10, 6))
input2 = layers.Input((10, 2, 5))
x1 = layers.Flatten()(input1)
x2 = layers.Flatten()(input2)

concat = layers.concatenate([x1, x2])

# Add hidden and dropout layers
hidden1 = layers.Dense(64, activation='relu')(concat)
hid1_out = layers.Dropout(0.5)(hidden1)
hidden2 = layers.Dense(32, activation='relu')(hid1_out)
hid2_out = layers.Dropout(0.5)(hidden2)
output = layers.Dense(1, activation='sigmoid')(hid2_out)

model = Model(inputs=[input1, input2], outputs=output)

# summarize layers
print(model.summary())

# compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


# fit the keras model on the dataset
history = model.fit([X1_train, X2_train], y_train, epochs=200, batch_size=5, verbose=0, validation_data=([X1_test, X2_test], y_test))

# evaluate the keras model
_, train_accuracy = model.evaluate([X1_train, X2_train], y_train, verbose=0)
_, test_accuracy = model.evaluate([X1_test, X2_test], y_test, verbose=0)
print('Accuracy NN: %.2f' % (train_accuracy*100))
print('Accuracy NN: %.2f' % (test_accuracy*100))

A problem occurs here. No plot is showing.

# Plots
from matplotlib import pyplot

pyplot.subplot(211)
pyplot.title('Loss')
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()

# plot accuracy
pyplot.subplot(212)
pyplot.title('Accuracy')
pyplot.plot(history.history['accuracy'], label='train')
pyplot.plot(history.history['val_accuracy'], label='test')
pyplot.legend()
pyplot.show(`

Could someone give me any hints on how to manage it ?
Thank you for giving me some of your time

Answer 1

below is the code for a function that will produce two plots side by side. The first plot shows the training loss and validation loss versus epochs. The second plot shows training accuracy and validation accuracy versus epochs. It also places a dot in the first plot for the epoch with the lowest validation loss and a dot on the second plot for the epoch with the highest validation accuracy.

def tr_plot(history):
    #Plot the training and validation data
    tacc=history.history['accuracy']
    tloss=history.history['loss']
    vacc=history.history['val_accuracy']
    vloss=history.history['val_loss']
    Epoch_count=len(tacc)
    Epochs=[]
    for i in range (Epoch_count):
        Epochs.append(i+1) 
    index_loss=np.argmin(vloss)#  this is the epoch with the lowest validation loss
    val_lowest=vloss[index_loss] # lowest validation loss value
    index_acc=np.argmax(vacc)  # this is the epoch with the highest training accuracy
    acc_highest=vacc[index_acc] # this is the highest accuracy value
    plt.style.use('fivethirtyeight')
    sc_label='best epoch= '+ str(index_loss+1 )
    vc_label='best epoch= '+ str(index_acc + 1)
    fig,axes=plt.subplots(nrows=1, ncols=2, figsize=(20,8))
    axes[0].plot(Epochs,tloss, 'r', label='Training loss')
    axes[0].plot(Epochs,vloss,'g',label='Validation loss' )
    axes[0].scatter(index_loss+1 ,val_lowest, s=150, c= 'blue', label=sc_label)
    axes[0].set_title('Training and Validation Loss')
    axes[0].set_xlabel('Epochs')
    axes[0].set_ylabel('Loss')
    axes[0].legend()
    axes[1].plot (Epochs,tacc,'r',label= 'Training Accuracy')
    axes[1].plot (Epochs,vacc,'g',label= 'Validation Accuracy')
    axes[1].scatter(index_acc+1 ,acc_highest, s=150, c= 'blue', label=vc_label)
    axes[1].set_title('Training and Validation Accuracy')
    axes[1].set_xlabel('Epochs')
    axes[1].set_ylabel('Accuracy')
    axes[1].legend()
    plt.tight_layout
    plt.show()

The resulting plot looks like this