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August 23, 2017 09:48
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| import common | |
| import tensorflow as tf | |
| from tensorflow.python.ops import rnn_cell | |
| from tensorflow.python.ops.rnn import bidirectional_rnn | |
| import time | |
| # Utility functions | |
| def weight_variable(shape): | |
| initial = tf.truncated_normal(shape, stddev=0.4) | |
| return tf.Variable(initial) | |
| def bias_variable(shape): | |
| #print(type(shape)) | |
| #time.sleep(300) | |
| initial = tf.constant(0.2, shape=shape) | |
| return tf.Variable(initial) | |
| def conv2d(x, W, stride=(1, 1), padding='SAME'): | |
| return tf.nn.conv2d(x, W, strides=[1, stride[0], stride[1], 1], | |
| padding=padding) | |
| def max_pool(x, ksize=(2, 2), stride=(2, 2)): | |
| return tf.nn.max_pool(x, ksize=[1, ksize[0], ksize[1], 1], | |
| strides=[1, stride[0], stride[1], 1], padding='SAME') | |
| def avg_pool(x, ksize=(2, 2), stride=(2, 2)): | |
| return tf.nn.avg_pool(x, ksize=[1, ksize[0], ksize[1], 1], | |
| strides=[1, stride[0], stride[1], 1], padding='SAME') | |
| def convolutional_layers(): | |
| x = tf.placeholder(tf.float32, [None, None, None]) | |
| # First layer | |
| W_conv1 = weight_variable([5, 5, 1, 48]) | |
| b_conv1 = bias_variable([48]) | |
| x_expanded = tf.expand_dims(x, 3) | |
| h_conv1 = tf.nn.relu(conv2d(x_expanded, W_conv1) + b_conv1) | |
| h_pool1 = max_pool(h_conv1, ksize=(2, 2), stride=(2, 2)) | |
| # Second layer | |
| W_conv2 = weight_variable([5, 5, 48, 64]) | |
| b_conv2 = bias_variable([64]) | |
| h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) | |
| h_pool2 = max_pool(h_conv2, ksize=(2, 1), stride=(2, 1)) | |
| # Third layer | |
| W_conv3 = weight_variable([5, 5, 64, 128]) | |
| b_conv3 = bias_variable([128]) | |
| h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3) | |
| h_pool3 = max_pool(h_conv3, ksize=(2, 2), stride=(2, 2)) | |
| return x, h_pool3, [W_conv1, b_conv1, | |
| W_conv2, b_conv2, | |
| W_conv3, b_conv3] | |
| def get_training_model(): | |
| x, conv_layer, conv_vars = convolutional_layers() | |
| # Densely connected layer | |
| W_fc1 = weight_variable([32 * 8 * 128, 2048]) | |
| b_fc1 = bias_variable([2048]) | |
| conv_layer_flat = tf.reshape(conv_layer, [-1, 32 * 8 * 128]) | |
| h_fc1 = tf.nn.relu(tf.matmul(conv_layer_flat, W_fc1) + b_fc1) | |
| # Output layer | |
| W_fc2 = weight_variable([2048, 1 + 7 * common.OUTPUT_SHAPE[0]]) | |
| b_fc2 = bias_variable([1 + 7 * common.OUTPUT_SHAPE[0]]) | |
| y = tf.matmul(h_fc1, W_fc2) + b_fc2 | |
| return (x, y, conv_vars + [W_fc1, b_fc1, W_fc2, b_fc2]) | |
| def get_train_model(): | |
| x,y, params = get_training_model() | |
| inputs = tf.placeholder(tf.float32, [None, None, common.OUTPUT_SHAPE[0]]) | |
| # Here we use sparse_placeholder that will generate a | |
| # SparseTensor required by ctc_loss op. | |
| targets = tf.sparse_placeholder(tf.int32) | |
| # 1d array of size [batch_size] | |
| seq_len = tf.placeholder(tf.int32, [None]) | |
| # Defining the cell for forward and backward layer | |
| forwardH1 = rnn_cell.LSTMCell(common.num_hidden, use_peepholes=True, state_is_tuple=True) | |
| backwardH1 = rnn_cell.LSTMCell(common.num_hidden, use_peepholes=True, state_is_tuple=True) | |
| # The second output previous state and is ignored | |
| outputs, _ = tf.nn.bidirectional_dynamic_rnn(forwardH1,backwardH1,x,seq_len,dtype=tf.float32) | |
| outputs=tf.concat(2,outputs) | |
| shape = tf.shape(inputs) | |
| batch_s, max_timesteps = shape[0], shape[1] | |
| weights = tf.Variable(tf.truncated_normal([common.num_hidden, | |
| common.num_classes], | |
| stddev=0.4), name="weights") | |
| # Reshaping to apply the same weights over the timesteps | |
| outputs = tf.reshape(outputs, [-1, 2*common.num_hidden]) | |
| # Truncated normal with mean 0 and stdev=0.1 | |
| #W = tf.Variable(tf.truncated_normal([2*common.num_hidden, common.num_classes], stddev=0.1), name="W") | |
| W = tf.Variable(tf.truncated_normal([2*common.num_hidden, common.num_classes], stddev=0.5), name="W") | |
| # Zero initialization | |
| b = tf.zeros(shape=[common.num_classes],name='b') | |
| #b = tf.ones(shape=[common.num_classes],name='b') | |
| # Doing the affine projection | |
| logits = tf.matmul(outputs, W)+b | |
| # Reshaping back to the original shape | |
| logits = tf.reshape(logits, [batch_s, -1, common.num_classes]) | |
| # Time major | |
| logits = tf.transpose(logits, (1, 0, 2)) | |
| return logits, inputs, targets, seq_len, W, b |
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