Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 5
Deep Learning and Neural Networks
def run_network(model=None, data=None, error=False):
global_start_time = time.time()
epochs = 2
ratio = 0.5
sequence_length = 100
X_train, y_train, X_test, y_test = process_data(
data, sequence_length, ratio,error)
print ('\nData Loaded. Compiling...\n')
if model is None:
model = build_model()
try:
model.fit(
X_train, y_train,
batch_size=512, nb_epoch=epochs, validation_
split=0.05)
predicted = model.predict(X_test)
predicted = np.reshape(predicted, (predicted.size,))
except KeyboardInterrupt:
print ('Training duration (s) : ', time.time() -
global_start_time)
return model, y_test, 0
try:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_test[:100]*result_max)
plt.plot(predicted[:100]*result_max)
plt.show()
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