def _initialize_vars(self):
"""Sets up the training graph."""
with tf.variable_scope(self.name) as scope:
self.global_step = tf.get_variable(
'global_step',
shape=[],
initializer=tf.zeros_initializer())
self.input = tf.placeholder(tf.float32, shape=[None, self.input_dim])
current = self.input
for i in range(self.encode_layers - 1):
current = self._relu_layer(current, self.input_dim, self.input_dim, i)
self.encoded = self._relu_layer(current, self.input_dim, self.hidden_units, self.encode_layers - 1)
# Make batch size the last dimension (for use with tf.nn.top_k)
encoded_t = tf.transpose(self.encoded)
# Compute the indices corresponding to the top k activations for each
# neuron in the final encoder layer
k = int(self.sparsity * self.batch_size)
_, top_indices = tf.nn.top_k(encoded_t, k=k, sorted=False)
# Transform top_indices, which contains rows of column indices, into
# indices, a list of [row, column] pairs (for use with tf.scatter_nd)
top_k_unstacked = tf.unstack(top_indices, axis=1)
row_indices = [tf.range(self.hidden_units) for _ in range(k)]
combined_columns = tf.transpose(tf.stack(_interleave(row_indices, top_k_unstacked)))
indices = tf.reshape(combined_columns, [-1, 2])
# Apply sparsity constraint
updates = tf.ones(self.hidden_units * k)
shape = tf.constant([self.hidden_units, self.batch_size])
mask = tf.scatter_nd(indices, updates, shape)
sparse_encoded = self.encoded * tf.transpose(mask)
self.decoded = self._decode_layer(sparse_encoded)
self.loss = tf.reduce_sum(tf.square(self.decoded - self.input))
self.optimizer_op = self.optimizer(self.learning_rate).minimize(
self.loss, self.global_step)
self.saver = tf.train.Saver(tf.global_variables())
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