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| import tensorflow.compat.v1 as tf | |
| tf.disable_v2_behavior() | |
| def dense_layer(inputs, output_units, bias=True, activation=None, batch_norm=None, | |
| dropout=None, scope='dense-layer', reuse=False): | |
| """ | |
| Applies a dense layer to a 2D tensor of shape [batch_size, input_units] | |
| to produce a tensor of shape [batch_size, output_units]. | |
| Args: | |
| inputs: Tensor of shape [batch size, input_units]. | |
| output_units: Number of output units. | |
| activation: activation function. | |
| dropout: dropout keep prob. | |
| Returns: | |
| Tensor of shape [batch size, output_units]. | |
| """ | |
| with tf.variable_scope(scope, reuse=reuse): | |
| W = tf.get_variable( | |
| name='weights', | |
| initializer=tf.compat.v1.variance_scaling_initializer(), | |
| shape=[shape(inputs, -1), output_units] | |
| ) | |
| z = tf.matmul(inputs, W) | |
| if bias: | |
| b = tf.get_variable( | |
| name='biases', | |
| initializer=tf.constant_initializer(), | |
| shape=[output_units] | |
| ) | |
| z = z + b | |
| if batch_norm is not None: | |
| z = tf.layers.batch_normalization(z, training=batch_norm, reuse=reuse) | |
| z = activation(z) if activation else z | |
| z = tf.nn.dropout(z, dropout) if dropout is not None else z | |
| return z | |
| def time_distributed_dense_layer( | |
| inputs, output_units, bias=True, activation=None, batch_norm=None, | |
| dropout=None, scope='time-distributed-dense-layer', reuse=False): | |
| """ | |
| Applies a shared dense layer to each timestep of a tensor of shape | |
| [batch_size, max_seq_len, input_units] to produce a tensor of shape | |
| [batch_size, max_seq_len, output_units]. | |
| Args: | |
| inputs: Tensor of shape [batch size, max sequence length, ...]. | |
| output_units: Number of output units. | |
| activation: activation function. | |
| dropout: dropout keep prob. | |
| Returns: | |
| Tensor of shape [batch size, max sequence length, output_units]. | |
| """ | |
| with tf.variable_scope(scope, reuse=reuse): | |
| W = tf.get_variable( | |
| name='weights', | |
| initializer=tf.compat.v1.variance_scaling_initializer(), | |
| shape=[shape(inputs, -1), output_units] | |
| ) | |
| z = tf.einsum('ijk,kl->ijl', inputs, W) | |
| if bias: | |
| b = tf.get_variable( | |
| name='biases', | |
| initializer=tf.constant_initializer(), | |
| shape=[output_units] | |
| ) | |
| z = z + b | |
| if batch_norm is not None: | |
| z = tf.layers.batch_normalization(z, training=batch_norm, reuse=reuse) | |
| z = activation(z) if activation else z | |
| z = tf.nn.dropout(z, dropout) if dropout is not None else z | |
| return z | |
| def shape(tensor, dim=None): | |
| """Get tensor shape/dimension as list/int""" | |
| if dim is None: | |
| return tensor.shape.as_list() | |
| else: | |
| return tensor.shape.as_list()[dim] | |
| def rank(tensor): | |
| """Get tensor rank as python list""" | |
| return len(tensor.shape.as_list()) | |