Hands-On Meta Learning with Python by Sudharsan Ravichandiran

Author:Sudharsan Ravichandiran
Language: eng
Format: mobi, epub
Tags: COM012050 - COMPUTERS / Image Processing, COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM044000 - COMPUTERS / Neural Networks
Publisher: Packt
Published: 2018-12-27T06:59:28+00:00

def zero_state(self, batch_size, dtype):

def expand(x, dim, N):

return tf.concat([tf.expand_dims(x, dim) for _ in range(N)], axis=dim)

with tf.variable_scope('init', reuse=self.reuse):

state = {

'controller_state': expand(tf.tanh(tf.get_variable('init_state', self.rnn_size,

initializer=tf.random_normal_initializer(mean=0.0, stddev=0.5))),

dim=0, N=batch_size),

'read_vector_list': [expand(tf.nn.softmax(tf.get_variable('init_r_%d' % i, [self.memory_vector_dim],

initializer=tf.random_normal_initializer(mean=0.0, stddev=0.5))),

dim=0, N=batch_size)

for i in range(self.read_head_num)],

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