def convolve(self, image, training, keep_prob):
layers = [1, 32, 64]
width = 28
conv_window = 3
feature_layer_size = 128 # maybe 1024
result = image
for index in range(len(layers) - 1):
result = layer.conv_relu(result, layers[index], layers[index + 1],
conv_window)
result = layer.resnet_block(result,
layers=layers[index + 1],
width=conv_window,
training=training)
result = layer.resnet_block(result,
layers=layers[index + 1],
width=conv_window,
training=training)
result = layer.max_pool(result)
width = int(round(width / 2.0))
result = layer.conv_relu(result,
layers[-1],
feature_layer_size,
width=width,
padding='VALID')
h_out = tf.reshape(result, [-1, feature_layer_size])
h_out_drop = tf.nn.dropout(h_out, keep_prob)
y = layer.fully_connected(h_out_drop, feature_layer_size, 10)
return y
def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5, padding="VALID")
result = layer.max_pool(result) # 12
result = layer.conv_relu(result, 18, 24, width=5, padding="VALID")
result = layer.max_pool(result) # 4
result = tf.nn.dropout(result, keep_prob)
return layer.conv(result, 24, 10, width=4, padding="VALID")
def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5)
result = layer.max_pool(result) # 14
result = layer.conv_relu(result, 18, 24, width=5)
result = layer.max_pool(result) # 7
return layer.drop_conv(keep_prob,
result,
24,
10,
width=7,
padding="VALID")
def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5)
result = layer.resnet_block(result, 18, 3, training, momentum=0.99)
result = layer.max_pool(result) # 14
result = layer.resnet_block(result, 18, 3, training, momentum=0.99)
result = layer.conv_relu(result, 18, 24, width=5)
result = layer.resnet_block(result, 24, 3, training, momentum=0.99)
result = layer.max_pool(result) # 7
result = layer.resnet_block(result, 24, 3, training, momentum=0.99)
result = layer.conv_relu(result, 24, 32, width=5, padding="VALID")
result = layer.resnet_block(result, 32, 3, training, momentum=0.99)
result = tf.nn.dropout(result, keep_prob)
return layer.conv(result, 32, 10, width=3, padding="VALID")
def convolve(self, image, training, keep_prob):
result = layer.conv_relu(image,
1,
18,
width=5,
stride=2,
padding="VALID")
return layer.conv(result, 18, 10, width=12, padding="VALID")
def convolve(self, image, training, keep_prob):
result = layer.batch_normalization(image, training)
result = layer.conv_relu(result, 1, 18, width=5, padding="VALID")
result = layer.max_pool(result) # 12
result = layer.resnet_block(result, 18, 3, training)
result = layer.resnet_block(result, 18, 3, training)
result = layer.max_pool(result) # 6
result = layer.conv_relu(result, 18, 24, width=1)
result = layer.resnet_narrow(result, 24, 3, training)
result = layer.resnet_narrow(result, 24, 3, training)
result = layer.max_pool(result) # 3
result = layer.conv_relu(result, 24, 32, width=1)
result = layer.resnet_narrow(result, 32, 3, training)
result = layer.resnet_narrow(result, 32, 3, training)
return layer.drop_conv(keep_prob,
result,
32,
10,
width=3,
padding="VALID")
def convolve(self, image, training, keep_prob):
result = image
result = layer.batch_normalization(result, training)
result = layer.conv(result, 1, 16, width=5, stride=2, padding="VALID")
result = tf.nn.tanh(result)
result = layer.conv(result, 16, 16, width=3, stride=2, padding="VALID")
result = tf.nn.tanh(result)
result = layer.conv(result, 16, 32, width=3, padding="VALID")
result = tf.nn.tanh(result)
result = layer.conv(result, 32, 32, width=3, padding="VALID")
result = tf.nn.tanh(result)
result = tf.nn.dropout(result, keep_prob)
result = layer.conv_relu(result, 32, 10, width=1, padding="VALID")
return result
def convolve(self, image, training, keep_prob):
result = layer.conv_relu(image, 1, 24, width=28, padding="VALID")
result = tf.nn.dropout(result, keep_prob)
return layer.conv(result, 24, 10, width=1, padding="VALID")
