最近公司要在AI領域發力，基於此，研究了Resnet50的圖片分類，網路上有很多的例子，但是都沒有做成平臺化，提供一個元件一樣供各個業務平臺使用，基於此，在Resnet50的基礎上，擴充套件了登陸，圖片類別新增，上傳圖片，自動離散圖片到訓練集與測試集，頁面api提供重新訓練按鈕，測試圖片分類結果頁面。

1，github

本文程式碼：

https://github.com/xvshu/ImageNet-Api

2，Resnet50實現

#-*- 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(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, basic_block, [2, 2, 2, 2])

    @staticmethod
    def build_resnet_34(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, basic_block, [3, 4, 6, 3])

    @staticmethod
    def build_resnet_50(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, bottleneck, [3, 4, 6, 3])

    @staticmethod
    def build_resnet_101(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, bottleneck, [3, 4, 23, 3])

    @staticmethod
    def build_resnet_152(input_shape, num_outputs):
        return ResnetBuilder.build(input_shape, num_outputs, bottleneck, [3, 8, 36, 3])



def check_print():
    # Create a Keras Model
    model=ResnetBuilder.build_resnet_34((3, 224, 224), 100)
    model.summary()

    # Save a PNG of the Model Build
    plot_model(model,to_file='ResNet.png')

    model.compile(optimizer='sgd',loss='categorical_crossentropy')
    print ('Model Compiled')



if __name__=='__main__':
    check_print()
 

3，主要頁面：

登陸：

主頁 ：

類別展示：

新增類別：

測試訓練結果：

TensorBoard：

4，常規性配置：

Parameter.py：

class http_state:
    Success="Success"
    Faile="Faile"

class img_file_path:
    File_Train="D:\\Data\\ai\\lenet\\train"
    File_Test="D:\\Data\\ai\\lenet\\test"

class httpResultWhiteMsg:
    @staticmethod
    def send(value):
        return '<div style="color:red;font-size:36px;">'+value+'</div>'

class Parameters:
    host='172.30.53.250'
    port=8899
    TensorBoard_port=6006
    model_path="D:\\Data\\ai\\model\\traffic_sign.model"
    user="admin"
    pwd="admin"
    object_map={"000":"driverCard","001":"hkb","002":"idcard","003":"jhz",
                "004":"relativesPhoto","005":"socialSecurity","006":"vehicleCard"}
    logdir="D:\\Data\\ai\\log"