class Cnn:
def __init__(self, config):
self.config = config
self.visualization = Visualization(self.config)
pass
def inference(self, x, mode_name):
histogram_summary = False if mode_name == self.config.MODE.VALIDATION else True
kernel_image_summary = False if mode_name == self.config.MODE.VALIDATION else True
activation_image_summary = False if mode_name == self.config.MODE.VALIDATION else True
with tf.name_scope('inputs'):
x = tf.reshape(x, [
-1, self.config.IMAGE_SIZE.HEIGHT,
self.config.IMAGE_SIZE.WIDTH, self.config.IMAGE_SIZE.CHANNELS
])
tf.summary.image("/inputs", x, max_outputs=4)
with tf.variable_scope('convolution1'):
convolution_1 = self.conv_layer(
input_tensor=x,
depth_in=self.config.IMAGE_SIZE.CHANNELS,
depth_out=64,
mode_name=mode_name,
histogram_summary=histogram_summary,
kernel_image_summary=kernel_image_summary,
activation_image_summary=activation_image_summary)
with tf.variable_scope('max_pooling1'):
max_pooling_1 = tf.layers.max_pooling2d(inputs=convolution_1,
pool_size=[2, 2],
strides=2)
with tf.variable_scope('convolution2'):
convolution_2 = self.conv_layer(
input_tensor=max_pooling_1,
depth_in=64,
depth_out=128,
mode_name=mode_name,
histogram_summary=histogram_summary,
kernel_image_summary=False,
activation_image_summary=activation_image_summary)
with tf.variable_scope('max_pooling2'):
max_pooling_2 = tf.layers.max_pooling2d(inputs=convolution_2,
pool_size=[2, 2],
strides=2)
with tf.variable_scope('convolution3'):
convolution_3 = self.conv_layer(
input_tensor=max_pooling_2,
depth_in=128,
depth_out=256,
mode_name=mode_name,
histogram_summary=histogram_summary,
kernel_image_summary=False,
activation_image_summary=activation_image_summary)
with tf.variable_scope('max_pooling3'):
max_pooling_3 = tf.layers.max_pooling2d(inputs=convolution_3,
pool_size=[2, 2],
strides=2)
with tf.variable_scope('convolution4'):
convolution_4 = self.conv_layer(
input_tensor=max_pooling_3,
depth_in=256,
depth_out=512,
mode_name=mode_name,
histogram_summary=histogram_summary,
kernel_image_summary=False,
activation_image_summary=activation_image_summary)
with tf.variable_scope('max_pooling4'):
max_pooling_4 = tf.layers.max_pooling2d(inputs=convolution_4,
pool_size=[2, 2],
strides=2)
with tf.variable_scope('dense1'):
max_pooling_3 = tf.reshape(max_pooling_4, [
max_pooling_4.shape[0].value, max_pooling_4.shape[1].value *
max_pooling_4.shape[2].value * max_pooling_4.shape[3].value
])
dense_1 = tf.layers.dense(inputs=max_pooling_3,
units=1024,
activation=activation.lrelu)
if mode_name == self.config.MODE.TRAINING:
tf.summary.histogram('dense1'.format(mode_name), dense_1)
with tf.variable_scope('logits'):
logits = tf.layers.dense(inputs=dense_1,
units=self.config.NUM_CLASSES,
activation=activation.lrelu)
if mode_name == self.config.MODE.TRAINING:
tf.summary.histogram('logits', logits)
tf.summary.histogram('softmax', tf.nn.softmax(logits=logits))
return logits
def _conv2d(self, x, weights, strides):
return tf.nn.conv2d(x, weights, strides=strides, padding='SAME')
def conv_layer(self,
mode_name,
input_tensor,
depth_in,
depth_out,
kernel_height=3,
kernel_width=3,
strides=(1, 1, 1, 1),
activation_fn=activation.lrelu,
histogram_summary=False,
kernel_image_summary=False,
activation_image_summary=False):
weights = tf.get_variable(
"weights", [kernel_height, kernel_width, depth_in, depth_out],
initializer=tf.truncated_normal_initializer(stddev=0.01))
biases = tf.get_variable("biases", [depth_out],
initializer=tf.constant_initializer(0.01))
convolutions = self._conv2d(input_tensor, weights, strides=strides)
activations = activation_fn(convolutions + biases,
self.config.LEAKY_RELU_ALPHA)
if histogram_summary:
tf.summary.histogram(mode_name + '_weights', weights)
tf.summary.histogram(mode_name + '_activations', activations)
if kernel_image_summary:
weights_image_grid = self.visualization.kernels_image_grid(
kernel=weights)
tf.summary.image(mode_name + '/features',
weights_image_grid,
max_outputs=1)
if activation_image_summary:
activation_image = self.visualization.activation_image(
activations=activations)
tf.summary.image("/activated", activation_image)
return activations
