利用50层ResNet实现手势数字的识别

from keras.layers import Dense,Flatten,Input,Activation,ZeroPadding2D,AveragePooling2D,BatchNormalization,Conv2D,Add,MaxPooling2D from keras.models import Model import matplotlib.pyplot as plt from keras.preprocessing import image from keras.applications.imagenet_utils import preprocess_input import resnets_utils import keras.backend as K import numpy as np from keras.initializers import glorot_uniform import tensorflow as tf import time """ 获取数据 并将标签转换成one-hot """ def convert_data(): train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes=resnets_utils.load_dataset() train_x=train_set_x_orig/255 test_x = test_set_x_orig / 255 train_y=resnets_utils.convert_to_one_hot(train_set_y_orig,6).T test_y = resnets_utils.convert_to_one_hot(test_set_y_orig, 6).T #print(train_y.shape) return train_x,train_y,test_x,test_y """ 三层卷积的 残差单元 输出尺寸和维度不会变化 """ def identity_block(X,f,filters,stage,block): conv_name_base='res'+str(stage)+block+'_branch' bn_name_base='bn'+str(stage)+block+'_branch' F1,F2,F3=filters X_shortcut=X # print('输入尺寸={}'.format(X.shape)) #first conv X=Conv2D(filters=F1,kernel_size=(1,1),strides=(1,1),padding='valid',name=conv_name_base+'2a', kernel_initializer=glorot_uniform(seed=0))(X) # print('输出尺寸={}'.format(X.shape)) X=BatchNormalization(axis=3,name=bn_name_base+'2a')(X) X=Activation('relu')(X) #second conv X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X) # print('输出尺寸={}'.format(X.shape)) X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X) X = Activation('relu')(X) #third conv X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X) # print('输出尺寸={}'.format(X.shape)) #ResNet X=Add()([X,X_shortcut]) X = Activation('relu')(X) # print('最终输出尺寸={}'.format(X.shape)) return X """ 三层卷积的 残差单元 输出尺寸和维度会变化 """ def convolutional_block(X,f,filters,stage,block,s=2): conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' F1, F2, F3 = filters X_shortcut = X # print('输入尺寸={}'.format(X.shape)) # first conv X = Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + '2a', kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X) X = Activation('relu')(X) # print('输出尺寸={}'.format(X.shape)) # second conv X = Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b', kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2b')(X) X = Activation('relu')(X) # print('输出尺寸={}'.format(X.shape)) #third conv X = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c', kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name=bn_name_base + '2c')(X) X = Activation('relu')(X) # print('输出尺寸={}'.format(X.shape)) #ResNet X_shortcut=Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + '1', kernel_initializer=glorot_uniform(seed=0))(X_shortcut) X_shortcut = BatchNormalization(axis=3, name=bn_name_base + '1')(X_shortcut) # print('原始输入X经过变化的输出尺寸={}'.format(X.shape)) X = Add()([X, X_shortcut]) X = Activation('relu')(X) # print('最终输出尺寸={}'.format(X.shape)) return X """ 50层残差网络 """ def ResNet50(input_shape=(64,64,3),classes=6): X_input=Input(input_shape) print('输入尺寸={}'.format(X_input.shape)) X=ZeroPadding2D((3,3))(X_input) print('补完零尺寸={}'.format(X.shape)) #Stage 1 X=Conv2D(filters=64,kernel_size=(7,7),strides=(2,2),name='conv1', kernel_initializer=glorot_uniform(seed=0))(X) print('第一次卷积尺寸={}'.format(X.shape)) X=BatchNormalization(axis=3,name='bn_conv1')(X) X=Activation('relu')(X) X=MaxPooling2D(pool_size=(3,3),strides=(2,2))(X) print('第一次池化尺寸={}'.format(X.shape)) #Stage 2 X=convolutional_block(X,f=3,filters=[64,64,256],stage=2,block='a',s=1) print('第一次convolutional_block尺寸={}'.format(X.shape)) X = identity_block(X, f=3, filters=[64, 64, 256], stage=2, block='b') X = identity_block(X, f=3, filters=[64, 64, 256], stage=2, block='c') print('两次identity_block尺寸={}'.format(X.shape)) #Stage 3 X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a', s=2) print('第二次convolutional_block尺寸={}'.format(X.shape)) X = identity_block(X, f=3, filters=[128, 128, 512], stage=3, block='b') X = identity_block(X, f=3, filters=[128, 128, 512], stage=3, block='c') X = identity_block(X, f=3, filters=[128, 128, 512], stage=3, block='d') print('三次identity_block尺寸={}'.format(X.shape)) #Stage 4 X = convolutional_block(X, f=3, filters=[256, 256, 1024], stage=4, block='a', s=2) print('第三次convolutional_block尺寸={}'.format(X.shape)) X = identity_block(X, f=3, filters=[256, 256, 1024], stage=4, block='b') X = identity_block(X, f=3, filters=[256, 256, 1024], stage=4, block='c') X = identity_block(X, f=3, filters=[256, 256, 1024], stage=4, block='d') X = identity_block(X, f=3, filters=[256, 256, 1024], stage=4, block='e') X = identity_block(X, f=3, filters=[256, 256, 1024], stage=4, block='f') print('五次identity_block尺寸={}'.format(X.shape)) #Stage 5 X = convolutional_block(X, f=3, filters=[512, 512, 2048], stage=5, block='a', s=2) print('第四次convolutional_block尺寸={}'.format(X.shape)) X = identity_block(X, f=3, filters=[512, 512, 2048], stage=5, block='b') X = identity_block(X, f=3, filters=[512, 512, 2048], stage=5, block='c') print('两次identity_block尺寸={}'.format(X.shape)) #Pool X=AveragePooling2D(pool_size=(2,2))(X) print('最后一次平均池化尺寸={}'.format(X.shape)) #OutPut Flatten+FULLYCONNECTED X=Flatten()(X) X=Dense(units=classes,activation='softmax',name='fc'+str(classes),kernel_initializer=glorot_uniform(seed=0))(X) #create model model=Model(inputs=X_input,outputs=X,name='ResNet50') return model def test_identity_block(): with tf.Session() as sess: np.random.seed(1) A_prev=tf.placeholder('float',[3,4,4,6]) X=np.random.randn(3,4,4,6) A=identity_block(A_prev,f=2,filters=[2,4,6],stage=1,block='a') init=tf.global_variables_initializer() sess.run(init) out=sess.run([A],feed_dict={A_prev:X,K.learning_phase():0}) # print('out=',out[0][1][1][0]) def test_convolutional_block(): #tf.reset_default_graph() with tf.Session() as sess: np.random.seed(1) A_prev=tf.placeholder('float',[3,4,4,6]) X=np.random.randn(3,4,4,6) A=convolutional_block(A_prev,f=2,filters=[2,4,6],stage=1,block='a',s=2) init = tf.global_variables_initializer() sess.run(init) out=sess.run(A,feed_dict={A_prev:X}) print('out=',out[0][0][0]) def test_ResNet50(): #定义好模型结构 Resnet50_model=ResNet50(input_shape=(64,64,3),classes=6) #选定训练参数 Resnet50_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) #获取训练集和测试集 train_x, train_y,