def generator(self, opts, noise, is_training, reuse=False):
"""Generator function, suitable for simple picture experiments.
Args:
noise: [num_points, dim] array, where dim is dimensionality of the
latent noise space.
is_training: bool, defines whether to use batch_norm in the train
or test mode.
Returns:
[num_points, dim1, dim2, dim3] array, where the first coordinate
indexes the points, which all are of the shape (dim1, dim2, dim3).
"""
output_shape = self._data.data_shape # (dim1, dim2, dim3)
# Computing the number of noise vectors on-the-go
dim1 = tf.shape(noise)[0]
num_filters = opts['g_num_filters']
with tf.variable_scope("GENERATOR", reuse=reuse):
height = output_shape[0] / 4
width = output_shape[1] / 4
h0 = ops.linear(opts, noise, num_filters * height * width,
scope='h0_lin')
h0 = tf.reshape(h0, [-1, height, width, num_filters])
h0 = ops.batch_norm(opts, h0, is_training, reuse, scope='bn_layer1')
# h0 = tf.nn.relu(h0)
h0 = ops.lrelu(h0)
_out_shape = [dim1, height * 2, width * 2, num_filters / 2]
# for 28 x 28 does 7 x 7 --> 14 x 14
h1 = ops.deconv2d(opts, h0, _out_shape, scope='h1_deconv')
h1 = ops.batch_norm(opts, h1, is_training, reuse, scope='bn_layer2')
# h1 = tf.nn.relu(h1)
h1 = ops.lrelu(h1)
_out_shape = [dim1, height * 4, width * 4, num_filters / 4]
# for 28 x 28 does 14 x 14 --> 28 x 28
h2 = ops.deconv2d(opts, h1, _out_shape, scope='h2_deconv')
h2 = ops.batch_norm(opts, h2, is_training, reuse, scope='bn_layer3')
# h2 = tf.nn.relu(h2)
h2 = ops.lrelu(h2)
_out_shape = [dim1] + list(output_shape)
# data_shape[0] x data_shape[1] x ? -> data_shape
h3 = ops.deconv2d(opts, h2, _out_shape,
d_h=1, d_w=1, scope='h3_deconv')
h3 = ops.batch_norm(opts, h3, is_training, reuse, scope='bn_layer4')
if opts['input_normalize_sym']:
return tf.nn.tanh(h3)
else:
return tf.nn.sigmoid(h3)
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