comiple.py

from keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt

#Optimiser 
adam = k.optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)

# Compile model
model.compile(optimizer=adam, loss=crf.loss_function, metrics=[crf.accuracy, 'accuracy'])

model.summary()

# Saving the best model only
filepath="ner-bi-lstm-td-model-{val_accuracy:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]

# Fit the best model
history = model.fit(X, np.array(y), batch_size=256, epochs=10, validation_split=0.2, verbose=1, callbacks=callbacks_list)

# Plot the graph 
plt.style.use('ggplot')

def plot_history(history):
    accuracy = history.history['accuracy']
    val_accuracy = history.history['val_accuracy']
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    x = range(1, len(acc) + 1)

    plt.figure(figsize=(12, 5))
    plt.subplot(1, 2, 1)
    plt.plot(x, acc, 'b', label='Training acc')
    plt.plot(x, val_acc, 'r', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.legend()
    plt.subplot(1, 2, 2)
    plt.plot(x, loss, 'b', label='Training loss')
    plt.plot(x, val_loss, 'r', label='Validation loss')
    plt.title('Training and validation loss')
    plt.legend()

plot_history(history)