Hands-On Deep Learning Algorithms with Python

Training the LSTM model

Start the TensorFlow session and initialize all the variables:

session = tf.Session()session.run(tf.global_variables_initializer())

Set the number of epochs:

epochs = 100

Then, for each iteration, perform the following:

for i in range(epochs):    train_predictions = []    index = 0    epoch_loss = []

Then sample a batch of data and train the network:

    while(index + batch_size) <= len(X_train):        X_batch = X_train[index:index+batch_size]        y_batch = y_train[index:index+batch_size]                #predict the price and compute the loss        predicted, loss_val, _ = session.run([y_hat, loss, optimizer], feed_dict={input:X_batch, target:y_batch})                #store the loss in the epoch_loss list        epoch_loss.append(loss_val) #store the predictions in the train_predictions ...