Python tensorflow.contrib.rnn.BasicLSTMCell() Examples
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Example #1
Source File: word_rnn.py From tensorflow-nlp-examples with MIT License | 7 votes |
def RNN(x, weights, biases): # reshape to [1, n_input] x = tf.reshape(x, [-1, n_input]) # Generate a n_input-element sequence of inputs # (eg. [had] [a] [general] -> [20] [6] [33]) x = tf.split(x, n_input, 1) # 2-layer LSTM, each layer has n_hidden units. # Average Accuracy= 95.20% at 50k iter rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden), rnn.BasicLSTMCell(n_hidden)]) # 1-layer LSTM with n_hidden units but with lower accuracy. # Average Accuracy= 90.60% 50k iter # Uncomment line below to test but comment out the 2-layer rnn.MultiRNNCell above # rnn_cell = rnn.BasicLSTMCell(n_hidden) # generate prediction outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32) # there are n_input outputs but # we only want the last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #2
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #3
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #4
Source File: tensorflow_LSTMs.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def LSTMs(x, weights, biases, timesteps , num_hidden): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #5
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #6
Source File: studyTF_LSTM_demo.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #7
Source File: studyTF_LSTM_demo.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #8
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #9
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #10
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #11
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #12
Source File: studyTF_LSTM_demo.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #13
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #14
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #15
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #16
Source File: tensorflow_LSTMs.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def LSTMs(x, weights, biases, timesteps , num_hidden): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #17
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #18
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #19
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #20
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #21
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #22
Source File: tensorflow_LSTMs.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def LSTMs(x, weights, biases, timesteps , num_hidden): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #23
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #24
Source File: studyTF_LSTM_demo.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #25
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #26
Source File: use_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #27
Source File: train_studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #28
Source File: use_TF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #29
Source File: studyTF_LSTM_demo_me.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']
Example #30
Source File: studyTF_LSTM_demo.py From DeepLearning_Wavelet-LSTM with MIT License | 6 votes |
def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out']