from tensorlayer.layers import InputLayer, TimeDistributedLayer, DenseLayer
sess = tf.InteractiveSession()
batch_size = 32
timestep = 20
input_dim = 100
## no reuse
x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep, input_dim], name="encode_seqs")
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
if net.outputs.get_shape().as_list() != [32, 20, 50]:
raise Exception("shape do not match")
# ... (32, 20, 50)
net.print_params(False)
if net.count_params() != 5050:
raise Exception("params do not match")
## reuse
def model(x, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
return net
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
print(n.outputs.shape)
# ... (10, 200, 2)
n.print_layers()
n.print_params(False)
shape = n.outputs.get_shape().as_list()
if shape != [10, 200, 16]:
raise Exception("shape dont match")
if len(n.all_layers) != 2:
raise Exception("layers dont match")
if len(n.all_params) != 2:
raise Exception("params dont match")
if n.count_params() != 416:
raise Exception("params dont match")
## 2D
x = tf.placeholder('float32', [10, 100, 100, 3], name='x')
n = InputLayer(x, name='in')
n = Conv2d(n, 32, (3, 2), (1, 1), padding='SAME', name='conv2d')
n = SubpixelConv2d(n, scale=2, name='subpixel2d')
print(n.outputs.shape)
n.print_layers()
n.print_params(False)
shape = n.outputs.get_shape().as_list()
if shape != [10, 200, 200, 8]:
raise Exception("shape dont match")
from tensorlayer.layers import InputLayer, TimeDistributedLayer, DenseLayer
sess = tf.InteractiveSession()
batch_size = 32
timestep = 20
input_dim = 100
## no reuse
x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep, input_dim], name="encode_seqs")
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
if net.outputs.get_shape().as_list() != [32, 20, 50]:
raise Exception("shape dont match")
# ... (32, 20, 50)
net.print_params(False)
if net.count_params() != 5050:
raise Exception("params dont match")
## reuse
def model(x, is_train=True, reuse=False):
with tf.variable_scope("model", reuse=reuse):
net = InputLayer(x, name='input')
net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units': 50, 'name': 'dense'}, name='time_dense')
return net
net_train = model(x, is_train=True, reuse=False)
net_test = model(x, is_train=False, reuse=True)
