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tf 1.x 에서 RNN & LSTM #3

Open abooundev opened 3 years ago

abooundev commented 3 years ago

tf 1.x 에서 RNN & LSTM

Using static_rnn()

n_inputs = 3
n_neurons = 5

#############################################################
reset_graph()

X0 = tf.placeholder(tf.float32, [None, n_inputs])
X1 = tf.placeholder(tf.float32, [None, n_inputs])

basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
output_seqs, states = tf.nn.static_rnn(basic_cell, [X0, X1],
                                       dtype=tf.float32)
Y0, Y1 = output_seqs

#############################################################
init = tf.global_variables_initializer()

X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]])
X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]])

#############################################################
with tf.Session() as sess:
    init.run()
    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})

Packing sequences

n_steps = 2
n_inputs = 3
n_neurons = 5

#############################################################
reset_graph()

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
X_seqs = tf.unstack(tf.transpose(X, perm=[1, 0, 2]))

basic_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
output_seqs, states = tf.nn.static_rnn(basic_cell, X_seqs,
                                       dtype=tf.float32)
outputs = tf.transpose(tf.stack(output_seqs), perm=[1, 0, 2])

#############################################################
init = tf.global_variables_initializer()
X_batch = np.array([
        # t = 0      t = 1 
        [[0, 1, 2], [9, 8, 7]], # instance 1
        [[3, 4, 5], [0, 0, 0]], # instance 2
        [[6, 7, 8], [6, 5, 4]], # instance 3
        [[9, 0, 1], [3, 2, 1]], # instance 4
    ])

#############################################################
with tf.Session() as sess:
    init.run()
    outputs_val = outputs.eval(feed_dict={X: X_batch})