人脸识别Python代码

import tensorflow as tf import numpy as np import os from numpy import genfromtxt from keras import backend as K from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate from keras.models import Model from keras.layers.normalization import BatchNormalization from keras.layers.pooling import MaxPooling2D, AveragePooling2D from FaceRecognitionForTheHappyHouse import fr_utils from keras.layers.core import Lambda, Flatten, Dense def inception_block_1a(X): """ Implementation of an inception block """ X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name ='inception_3a_3x3_conv1')(X) X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name = 'inception_3a_3x3_bn1')(X_3x3) X_3x3 = Activation('relu')(X_3x3) X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3) X_3x3 = Conv2D(128, (3, 3), data_format='channels_first', name='inception_3a_3x3_conv2')(X_3x3) X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_3x3_bn2')(X_3x3) X_3x3 = Activation('relu')(X_3x3) X_5x5 = Conv2D(16, (1, 1), data_format='channels_first', name='inception_3a_5x5_conv1')(X) X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn1')(X_5x5) X_5x5 = Activation('relu')(X_5x5) X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5) X_5x5 = Conv2D(32, (5, 5), data_format='channels_first', name='inception_3a_5x5_conv2')(X_5x5) X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn2')(X_5x5) X_5x5 = Activation('relu')(X_5x5) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3a_pool_conv')(X_pool) X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_pool_bn')(X_pool) X_pool = Activation('relu')(X_pool) X_pool = ZeroPadding2D(padding=((3, 4), (3, 4)), data_format='channels_first')(X_pool) X_1x1 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3a_1x1_conv')(X) X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_1x1_bn')(X_1x1) X_1x1 = Activation('relu')(X_1x1) # CONCAT inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1) return inception def inception_block_1b(X): X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_3b_3x3_conv1')(X) X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn1')(X_3x3) X_3x3 = Activation('relu')(X_3x3) X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3) X_3x3 = Conv2D(128, (3, 3), data_format='channels_first', name='inception_3b_3x3_conv2')(X_3x3) X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn2')(X_3x3) X_3x3 = Activation('relu')(X_3x3) X_5x5 = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3b_5x5_conv1')(X) X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn1')(X_5x5) X_5x5 = Activation('relu')(X_5x5) X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5) X_5x5 = Conv2D(64, (5, 5), data_format='channels_first', name='inception_3b_5x5_conv2')(X_5x5) X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn2')(X_5x5) X_5x5 = Activation('relu')(X_5x5) X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X) X_pool = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_pool_conv')(X_pool) X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_pool_bn')(X_pool) X_pool = Activation('relu')(X_pool) X_pool = ZeroPadding2D(padding=(4, 4), data_format='channels_first')(X_pool) X_1x1 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_1x1_conv')(X) X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_1x1_bn')(X_1x1) X_1x1 = Activation('relu')(X_1x1) inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1) return inception def inception_block_1c(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_3c_3x3', cv1_out=128, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_3c_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = ZeroPadding2D(padding=((0, 1), (0, 1)), data_format='channels_first')(X_pool) inception = concatenate([X_3x3, X_5x5, X_pool], axis=1) return inception def inception_block_2a(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=192, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4a_5x5', cv1_out=32, cv1_filter=(1, 1), cv2_out=64, cv2_filter=(5, 5), cv2_strides=(1, 1), padding=(2, 2)) X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool, layer='inception_4a_pool', cv1_out=128, cv1_filter=(1, 1), padding=(2, 2)) X_1x1 = fr_utils.conv2d_bn(X, layer='inception_4a_1x1', cv1_out=256, cv1_filter=(1, 1)) inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1) return inception def inception_block_2b(X): #inception4e X_3x3 = fr_utils.conv2d_bn(X, layer='inception_4e_3x3', cv1_out=160, cv1_filter=(1, 1), cv2_out=256, cv2_filter=(3, 3), cv2_strides=(2, 2), padding=(1, 1)) X_5x5 = fr_utils.conv2d_bn(X, layer='inception_4e_5x5', cv1_out=64, cv1_filter=(1, 1), cv2_out=128, cv2_filter=(5, 5), cv2_strides=(2, 2), padding=(2, 2)) X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X) X_pool = ZeroPadding2D(padding=((0, 1), (0, 1)), data_format='channels_first')(X_pool) inception = concatenate([X_3x3, X_5x5, X_pool], axis=1) return inception def inception_block_3a(X): X_3x3 = fr_utils.conv2d_bn(X, layer='inception_5a_3x3', cv1_out=96, cv1_filter=(1, 1), cv2_out=384, cv2_filter=(3, 3), cv2_strides=(1, 1), padding=(1, 1)) X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X) X_pool = fr_utils.conv2d_bn(X_pool,