def inference(self):
with tf.device(self.devices[0]):
x = self.x * self.pw
x = self.batch_norm_layer(x, scope="pw_input")
x = 2 * tf.tanh(x)
x = tf.unstack(self.x, self.in_steps, 1)
convLSTM2D_cell = rnn.Conv2DLSTMCell(
input_shape=self.input_shape,
output_channels=self.filters[0],
kernel_shape=[3, 3],
forget_bias=1.0,
initializers=orthogonal_initializer(),
name="conv_lstm_cell_{}".format(self.filters[0]))
dropout_cell = DropoutWrapper(convLSTM2D_cell,
input_keep_prob=self.keep_rate,
output_keep_prob=self.keep_rate,
state_keep_prob=self.keep_rate)
outputs, states = tf.nn.static_rnn(dropout_cell,
x,
dtype=tf.float32)
scope = "activation_batch_norm_{}".format(self.filters[0])
outputs = self.batch_norm_layer(outputs,
scope=scope,
activation_fn=tf.nn.tanh)
outputs = 2 * outputs
x = tf.unstack(outputs, self.in_steps, 0)
with tf.device(self.devices[-1]):
for filter in self.filters[1:]:
convLSTM2D_cell = rnn.Conv2DLSTMCell(
input_shape=self.input_shape,
output_channels=filter,
kernel_shape=[3, 3],
forget_bias=1.0,
initializers=orthogonal_initializer(),
name="conv_lstm_cell_{}".format(filter))
dropout_cell = DropoutWrapper(convLSTM2D_cell,
input_keep_prob=self.keep_rate,
output_keep_prob=self.keep_rate,
state_keep_prob=self.keep_rate)
outputs, states = tf.nn.static_rnn(dropout_cell,
x,
dtype=tf.float32)
scope = "activation_batch_norm_{}".format(filter)
outputs = self.batch_norm_layer(outputs,
scope=scope,
activation_fn=tf.nn.tanh)
outputs = 2 * outputs
x = tf.unstack(outputs, self.in_steps, 0)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
outputs = outputs[-self.out_steps:]
self.y_hat = tf.transpose(outputs, perm=[1, 0, 2, 3, 4])
return self.y_hat
def forward_pass(self, x):
x = tf.unstack(x, self.in_steps, 1)
filters = [self.input_shape[-1]] + self.filters
for i in range(1, len(filters)):
filter = filters[i]
input_shape = self.input_shape[:-1] + [filters[i - 1]]
convLSTM2D_cell = rnn.Conv2DLSTMCell(
input_shape=input_shape,
output_channels=filter,
kernel_shape=[3, 3],
forget_bias=1.0,
initializers=orthogonal_initializer(),
name="conv_lstm_cell_{}".format(i))
dropout_cell = DropoutWrapper(convLSTM2D_cell,
input_keep_prob=self.keep_rate,
output_keep_prob=self.keep_rate,
state_keep_prob=self.keep_rate)
outputs, states = tf.nn.static_rnn(dropout_cell,
x,
dtype=tf.float32)
outputs = self._batch_norm(outputs)
x = tf.unstack(outputs, self.in_steps, 0)
outputs = outputs[-self.out_steps:]
y_hat = tf.transpose(outputs, perm=[1, 0, 2, 3, 4])
return y_hat
def conv2dlstm(self, x, h):
x = self.to_nhwc(x) # NCHW conv not available for Conv2DLSTM
# conv2dlstm operation
layer = rnn.Conv2DLSTMCell(
name='conv_2d_lstm',
input_shape=[32, 32, 75],
output_channels=96,
kernel_shape=[3, 3],
) # different versions of TensorFlow have different default names
layer._base_name = 'conv_2d_lstm' # tf bug. does not take name we pass (arg is not forward to parent class)
y, h = layer.apply(x, h)
y = tf.nn.relu(y)
y = self.from_nhwc(y)
return y, h
import tensorboard
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import rnn
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
# mnist = mnist.train.images.reshape(-1, 28, 28)
input_image = tf.placeholder(tf.float32, [None, 28, 28, 1])
Y = tf.placeholder(tf.float32, [None, 10])
# conv1 = tf.layers.conv2d(inputs=input_image, filters=32, kernel_size=[5, 5], padding='same', activation=tf.nn.relu)
# pool1 = tf.layers.max_pooling2d(inputs=conv1,pool_size=[4,4],strides=3)
# conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu)
# pool2 = tf.layers.max_pooling2d(inputs=conv2,pool_size=[3,3],strides=2)
# dense1 = tf.layers.dense(inputs=tf.layers.flatten(pool2),units=256)
# dense2 = tf.layers.dense(inputs=dense1,units=10)
conv1 = rnn.Conv2DLSTMCell()
conv1 = tf.layers.conv2d(inputs=input_image,
filters=32,
kernel_size=[5, 5],
padding='same',
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[4, 4], strides=3)
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[3, 3],
padding='same',
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[3, 3], strides=2)
dense1 = tf.layers.dense(inputs=tf.layers.flatten(pool2), units=256)
dense2 = tf.layers.dense(inputs=dense1, units=10)
