基于Keras+Tensorflow搭建，提供ResNet50神经网络的图片分类平台.zip

#-*- coding: UTF-8 -*- """ Environment: Keras2.0.5，Python2.7 Model: ResNet """ from __future__ import division from keras.models import Model from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D from keras.layers import Input, Activation, Dense, Flatten from keras.layers.merge import add from keras.layers.normalization import BatchNormalization from keras.regularizers import l2 from keras import backend as K from keras.utils import plot_model import six def _handle_dim_ordering(): global ROW_AXIS global COL_AXIS global CHANNEL_AXIS if K.image_dim_ordering() == 'tf': ROW_AXIS = 1 COL_AXIS = 2 CHANNEL_AXIS = 3 else: CHANNEL_AXIS = 1 ROW_AXIS = 2 COL_AXIS = 3 def _get_block(identifier): if isinstance(identifier, six.string_types): res = globals().get(identifier) if not res: raise ValueError('Invalid {}'.format(identifier)) return res return identifier def _bn_relu(input): """ Helper to build a BN -> relu block """ norm = BatchNormalization(axis=CHANNEL_AXIS)(input) return Activation("relu")(norm) def _conv_bn_relu(**conv_params): """ Helper to build a conv -> BN -> relu block """ filters = conv_params["filters"] kernel_size = conv_params["kernel_size"] strides = conv_params.setdefault("strides", (1, 1)) kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal") padding = conv_params.setdefault("padding", "same") kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4)) def f(input): conv = Conv2D(filters=filters, kernel_size=kernel_size,strides=strides, padding=padding,kernel_initializer=kernel_initializer,kernel_regularizer=kernel_regularizer)(input) return _bn_relu(conv) return f def _bn_relu_conv(**conv_params): """ Helper to build a BN -> relu -> conv block. This is an improved scheme proposed in http://arxiv.org/pdf/1603.05027v2.pdf """ filters = conv_params["filters"] kernel_size = conv_params["kernel_size"] strides = conv_params.setdefault("strides", (1, 1)) kernel_initializer = conv_params.setdefault("kernel_initializer", "he_normal") padding = conv_params.setdefault("padding", "same") kernel_regularizer = conv_params.setdefault("kernel_regularizer", l2(1.e-4)) def f(input): activation = _bn_relu(input) return Conv2D(filters=filters, kernel_size=kernel_size,strides=strides, padding=padding, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)(activation) return f def _shortcut(input, residual): """ Adds a shortcut between input and residual block and merges them with "sum" """ # Expand channels of shortcut to match residual. # Stride appropriately to match residual (width, height) # Should be int if network architecture is correctly configured. input_shape = K.int_shape(input) residual_shape = K.int_shape(residual) stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS])) stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS])) equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS] shortcut = input # 1 X 1 conv if shape is different. Else identity. if stride_width > 1 or stride_height > 1 or not equal_channels: shortcut = Conv2D(filters=residual_shape[CHANNEL_AXIS], kernel_size=(1, 1), strides=(stride_width, stride_height), padding="valid", kernel_initializer="he_normal",kernel_regularizer=l2(0.0001))(input) return add([shortcut, residual]) def _residual_block(block_function, filters, repetitions, is_first_layer=False): """ Builds a residual block with repeating bottleneck blocks. """ def f(input): for i in range(repetitions): init_strides = (1, 1) if i == 0 and not is_first_layer: init_strides = (2, 2) input = block_function(filters=filters, init_strides=init_strides, is_first_block_of_first_layer=(is_first_layer and i == 0))(input) return input return f def basic_block(filters, init_strides=(1, 1), is_first_block_of_first_layer=False): """ Basic 3 X 3 convolution blocks for use on resnets with layers <= 34. Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf """ def f(input): if is_first_block_of_first_layer: # don't repeat bn->relu since we just did bn->relu->maxpool conv1 = Conv2D(filters=filters, kernel_size=(3, 3),strides=init_strides, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(input) else: conv1 = _bn_relu_conv(filters=filters, kernel_size=(3, 3), strides=init_strides)(input) residual = _bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv1) return _shortcut(input, residual) return f def bottleneck(filters, init_strides=(1, 1), is_first_block_of_first_layer=False): """ Bottleneck architecture for > 34 layer resnet. Follows improved proposed scheme in http://arxiv.org/pdf/1603.05027v2.pdf Returns: A final conv layer of filters * 4 """ def f(input): if is_first_block_of_first_layer: # don't repeat bn->relu since we just did bn->relu->maxpool conv_1_1 = Conv2D(filters=filters, kernel_size=(1, 1), strides=init_strides, padding="same", kernel_initializer="he_normal", kernel_regularizer=l2(1e-4))(input) else: conv_1_1 = _bn_relu_conv(filters=filters, kernel_size=(1, 1), strides=init_strides)(input) conv_3_3 = _bn_relu_conv(filters=filters, kernel_size=(3, 3))(conv_1_1) residual = _bn_relu_conv(filters=filters * 4, kernel_size=(1, 1))(conv_3_3) return _shortcut(input, residual) return f class ResnetBuilder(object): @staticmethod def build(input_shape, num_outputs, block_fn, repetitions): """ Builds a custom ResNet like architecture. Args: input_shape: The input shape in the form (nb_channels, nb_rows, nb_cols) num_outputs: The number of outputs at final softmax layer block_fn: The block function to use. This is either `basic_block` or `bottleneck`.The original paper used basic_block for layers < 50 repetitions: Number of repetitions of various block units.At each block unit, the number of filters are doubled and the input size is halved Returns: The keras `Model`. """ _handle_dim_ordering() if len(input_shape) != 3: raise Exception("Input shape should be a tuple (nb_channels, nb_rows, nb_cols)") # # Permute dimension order if necessary # if K.image_dim_ordering() == 'tf': # input_shape = (input_shape[1], input_shape[2], input_shape[0]) # Load function from str if needed. block_fn = _get_block(block_fn) input = Input(shape=input_shape) conv1 = _conv_bn_relu(filters=64, kernel_size=(7, 7), strides=(2, 2))(input) pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same")(conv1) block = pool1 filters = 64 for i, r in enumerate(repetitions): block = _residual_block(block_fn, filters=filters, repetitions=r, is_first_layer=(i == 0))(block) filters *= 2 # Last activation block = _bn_relu(block) # Classifier block block_shape = K.int_shape(block) pool2 = AveragePooling2D(pool_size=(block_shape[ROW_AXIS], block_shape[COL_AXIS]), strides=(1, 1))(block) flatten1 = Flatten()(pool2) dense = Dense(units=num_outputs, kernel_initializer="he_normal", activation="softmax")(flatten1) model = Model(inputs=input, outputs=dense) return model @staticmethod def build_resnet_18(i