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")