1-6-forward.py

# Forward pass
current_state = init_state
states_series = []
for current_input in inputs_series:
    current_input = tf.reshape(current_input, [batch_size, 1])
    input_and_state_concatenated = tf.concat(1, [current_input, current_state])  # Increasing number of columns

    next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)  # Broadcasted addition
    states_series.append(next_state)
    current_state = next_state

# Forward pass
current_state = init_state
states_series = []
for current_input in inputs_series:
    current_input = tf.reshape(current_input, [batch_size, 1])
    input_and_state_concatenated = tf.concat([current_input, current_state], 1)  # Increasing number of columns

    next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)  # Broadcasted addition
    states_series.append(next_state)
    current_state = next_state