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- import numpy as np
- import tensorflow as tf
-
-
- def tf_lstm(x, y_):
- '''
- LSTM model in TensorFlow, for MNIST dataset.
-
- Parameters:
- x: Variable(tensorflow.python.framework.ops.Tensor), shape (N, dims)
- y_: Variable(tensorflow.python.framework.ops.Tensor), shape (N, num_classes)
- Return:
- loss: Variable(tensorflow.python.framework.ops.Tensor), shape (1,)
- y: Variable(tensorflow.python.framework.ops.Tensor), shape (N, num_classes)
- '''
-
- print("Building LSTM model in tensorflow...")
- diminput = 28
- dimhidden = 128
- dimoutput = 10
- nsteps = 28
-
- forget_gate_w = tf.Variable(np.random.normal(
- scale=0.1, size=(diminput, dimhidden)).astype(np.float32))
- forget_gate_u = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden, dimhidden)).astype(np.float32))
- forget_gate_b = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden,)).astype(np.float32))
- input_gate_w = tf.Variable(np.random.normal(
- scale=0.1, size=(diminput, dimhidden)).astype(np.float32))
- input_gate_u = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden, dimhidden)).astype(np.float32))
- input_gate_b = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden,)).astype(np.float32))
- output_gate_w = tf.Variable(np.random.normal(
- scale=0.1, size=(diminput, dimhidden)).astype(np.float32))
- output_gate_u = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden, dimhidden)).astype(np.float32))
- output_gate_b = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden,)).astype(np.float32))
- tanh_w = tf.Variable(np.random.normal(
- scale=0.1, size=(diminput, dimhidden)).astype(np.float32))
- tanh_u = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden, dimhidden)).astype(np.float32))
- tanh_b = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden,)).astype(np.float32))
- out_weights = tf.Variable(np.random.normal(
- scale=0.1, size=(dimhidden, dimoutput)).astype(np.float32))
- out_bias = tf.Variable(np.random.normal(
- scale=0.1, size=(dimoutput,)).astype(np.float32))
- initial_state = tf.zeros((tf.shape(x)[0], dimhidden), dtype=tf.float32)
-
- last_c_state = initial_state
- last_h_state = initial_state
-
- for i in range(nsteps):
- cur_x = tf.slice(x, (0, i * diminput), (-1, diminput))
- # forget gate
- cur_forget = tf.matmul(last_h_state, forget_gate_u) + \
- tf.matmul(cur_x, forget_gate_w) + forget_gate_b
- cur_forget = tf.sigmoid(cur_forget)
- # input gate
- cur_input = tf.matmul(last_h_state, input_gate_u) + \
- tf.matmul(cur_x, input_gate_w) + input_gate_b
- cur_input = tf.sigmoid(cur_input)
- # output gate
- cur_output = tf.matmul(last_h_state, output_gate_u) + \
- tf.matmul(cur_x, output_gate_w) + output_gate_b
- cur_output = tf.sigmoid(cur_output)
- # tanh
- cur_tanh = tf.matmul(last_h_state, tanh_u) + \
- tf.matmul(cur_x, tanh_w) + tanh_b
- cur_tanh = tf.tanh(cur_tanh)
-
- last_c_state = last_c_state * cur_forget + cur_input * cur_tanh
- last_h_state = tf.tanh(last_c_state) * cur_output
-
- y = tf.matmul(last_h_state, out_weights) + out_bias
- loss = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
- loss = tf.reduce_mean(loss)
- return loss, y
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