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资料的下载真的很感谢(14条消息) 【中文】【吴恩达课后编程作业】Course 4 - 卷积神经网络 - 第二周作业_何宽的博客-CSDN博客
【博主使用的python版本：3.6.8】
对于此作业，您将使用 Keras。
在进入问题之前，请运行下面的单元格以加载所需的包。
import tensorflow as tf import numpy as np import scipy.misc from tensorflow.keras.applications.resnet_v2 import ResNet50V2 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet_v2 import preprocess_input, decode_predictions from tensorflow.keras import layers from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D from tensorflow.keras.models import Model, load_model from resnets_utils import * from tensorflow.keras.initializers import random_uniform, glorot_uniform, constant, identity from tensorflow.python.framework.ops import EagerTensor from matplotlib.pyplot import imshow from test_utils import summary, comparator import public_tests
- 非常深的神经网络的问题
- 非常深度网络的主要好处是它可以表示非常复杂的功能。它还可以学习许多不同抽象级别的特征，从边缘（在较浅的层，更接近输入）到非常复杂的特征（在更深的层，更接近输出）。
- 但是，使用更深的网络并不总是有帮助。训练梯度的一个巨大障碍是梯度消失：非常深的网络通常有一个梯度信号，很快就会归零，从而使梯度下降变得非常慢。
- 更具体地说，在梯度下降期间，当您从最后一层反向传播回第一层时，您将乘以每一步的权重矩阵，因此梯度可以呈指数级迅速减小到零（或者在极少数情况下，指数级快速增长并“爆炸”，因为获得非常大的值）。
- 因此，在训练过程中，您可能会看到随着训练的进行，较浅层的梯度的大小（或范数）会非常迅速地减小到零，如下所示：
构建一个残差网络
- 左图显示了通过网络的“主要路径”。右侧的图像将快捷方式添加到主路径。通过将这些 ResNet 块堆叠在一起，您可以形成一个非常深的网络。
- 讲座提到，使用带有快捷方式的 ResNet 块也使其中一个块学习恒等函数变得非常容易。这意味着您可以堆叠额外的 ResNet 块，而几乎没有损害训练集性能的风险。
- 在这一点上，还有一些证据表明，学习恒等函数的便利性解释了ResNets的卓越性能，甚至超过了跳过连接对梯度消失的帮助。
- ResNet 中使用两种主要类型的块，主要取决于输入/输出尺寸是相同还是不同。您将实现它们：“标识块”和“卷积块”。
恒等块（Identity block）
恒等块是 ResNet 中使用的标准块，对应于输入激活（例如a[L]）与输出激活（例如a[L+!]）具有相同维度的情况。为了充实 ResNet 身份块中发生的不同步骤，下面是一个显示各个步骤的替代图表：
上面的路径是“捷径”。较低的路径是“主要路径”。在此图中，请注意每层中的 CONV2D 和 ReLU 步骤。为了加快训练速度，添加了 BatchNorm 步骤。不要担心这很难实现 - 你会看到BatchNorm只是Keras中的一行代码！
在本练习中，您将实际实现此标识块的一个功能稍微强大的版本，其中跳过连接“跳过”3 个隐藏层而不是 2 个层。它看起来像这样：
主路径的第一部分：
- 第一个 CONV2D 具有F1形状为 （1，1） 和步幅为 （1，1） 的过滤器。它的填充是“有效的”。使用 0 作为随机统一初始化的种子：
kernel_initializer = initializer(seed=0). - 第一个 BatchNorm 是规范化“channel”轴。
- 然后应用 ReLU 激活函数。这没有超参数。
主路径的第二部分：
- 第二个 CONV2D 具有F2形状(f,f)和步幅为 （1，1） 的过滤器。它的填充是“相同的”。使用 0 作为随机统一初始化的种子：kernel_initializer = initializer(seed=0)
- 第二个 BatchNorm 是规范化“channel”轴。
- 然后应用 ReLU 激活函数。这没有超参数。
主路径的第三部分：
- 第三个 CONV2D 具有F3形状 （1，1） 和步幅为 （1，1） 的过滤器。它的填充是“相同的”。使用 0 作为随机统一初始化的种子：kernel_initializer = initializer(seed=0)
- 第三个 BatchNorm 是规范化“channel”轴。
- 然后应用 ReLU 激活函数。这没有超参数。
主路径的最后一部分：
- 第 3 层 X 的X_shortcut和输出相加。
- 提示：语法看起来像 Add（）（[var1，var2]）
- 然后应用 ReLU 激活函数。这没有超参数。
接下来我们就要实现残差网络的恒等块了
我们已将初始值设定项参数添加到函数中。此参数接收一个初始值设定项函数，类似于包 tensorflow.keras.initializers 或任何其他自定义初始值设定项中包含的函数。默认情况下，它将设置为random_uniform
请记住，这些函数接受种子参数，该参数可以是所需的任何值，但在此笔记本中必须将其设置为 0 才能进行评分。
下面是实际使用函数式 API 的强大功能来创建快捷方式路径的地方：
def identity_block(X, f, filters, training=True, initializer=random_uniform): """ 实现图 4 中定义的恒等块 Arguments: X -- 形状的输入张量（m、n_H_prev、n_W_prev、n_C_prev） f -- 整数，指定主路径中间 CONV 窗口的形状 filters -- python 整数列表，定义主路径的 CONV 层中的过滤器数量 训练 -- True：在训练模式下行为 错误：在推理模式下行为 初始值设定项 -- 设置图层的初始权重。等于随机统一初始值设定项 Returns: X -- output of the identity block, tensor of shape (n_H, n_W, n_C) """ # Retrieve Filters F1, F2, F3 = filters # Save the input value. You'll need this later to add back to the main path. X_shortcut = X cache = [] # 主路径的第一个组成部分 X = Conv2D(filters = F1, kernel_size = 1, strides = (1,1), padding = 'valid', kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training = training) # Default axis X = Activation('relu')(X) ### START CODE HERE ## Second component of main path (≈3 lines) X = Conv2D(filters = F2, kernel_size = f,strides = (1, 1),padding='same',kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training=training) X = Activation('relu')(X) ## Third component of main path (≈2 lines) X = Conv2D(filters = F3, kernel_size = 1, strides = (1, 1), padding='valid', kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training=training) ## Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines) X = Add()([X_shortcut,X]) X = Activation('relu')(X) ### END CODE HERE return X
我们来测试一下：
np.random.seed(1) X1 = np.ones((1, 4, 4, 3)) * -1 X2 = np.ones((1, 4, 4, 3)) * 1 X3 = np.ones((1, 4, 4, 3)) * 3 #按着X1，X2，X3的顺序排序 X = np.concatenate((X1, X2, X3), axis = 0).astype(np.float32) A3 = identity_block(X, f=2, filters=[4, 4, 3], initializer=lambda seed=0:constant(value=1), training=False) print(' 33[1mWith training=False 33[0mn') A3np = A3.numpy() print(np.around(A3.numpy()[:,(0,-1),:,:].mean(axis = 3), 5)) resume = A3np[:,(0,-1),:,:].mean(axis = 3) print(resume[1, 1, 0]) print('n 33[1mWith training=True 33[0mn') np.random.seed(1) A4 = identity_block(X, f=2, filters=[3, 3, 3], initializer=lambda seed=0:constant(value=1), training=True) print(np.around(A4.numpy()[:,(0,-1),:,:].mean(axis = 3), 5)) public_tests.identity_block_test(identity_block)
With training=False [[[ 0. 0. 0. 0. ] [ 0. 0. 0. 0. ]] [[192.71234 192.71234 192.71234 96.85617] [ 96.85617 96.85617 96.85617 48.92808]] [[578.1371 578.1371 578.1371 290.5685 ] [290.5685 290.5685 290.5685 146.78426]]] 96.85617 With training=True [[[0. 0. 0. 0. ] [0. 0. 0. 0. ]] [[0.40739 0.40739 0.40739 0.40739] [0.40739 0.40739 0.40739 0.40739]] [[4.99991 4.99991 4.99991 3.25948] [3.25948 3.25948 3.25948 2.40739]]] All tests passed!
卷积块
ResNet“卷积块”是第二种块类型。当输入和输出维度不匹配时，可以使用这种类型的块。与标识块的区别在于快捷方式路径中有一个 CONV2D 层：
- 快捷路径中的 CONV2D 层用于将输入调整为不同的维度，以便尺寸在将快捷方式值添加回主路径所需的最终添加中匹配。（这与讲座中讨论的矩阵的作用类似。）
- 例如，要将激活维度的高度和宽度减少 2 倍，可以使用步幅为 2 的 1x1 卷积。
- 快捷方式路径上的 CONV2D 层不使用任何非线性激活函数。它的主要作用是仅应用一个（学习的）线性函数来减小输入的维度，以便维度与后面的加法步骤相匹配。
- 对于前面的练习，出于评分目的需要额外的初始值设定项参数，并且默认情况下已将其设置为 glorot_uniform
主路径的第一个组成部分：
- 第一个 CONV2D 具有F1个形状为 （1，1） 和步幅为 （s，s） 的过滤器。它的填充是“有效的”。使用 0 作为种子glorot_uniform kernel_initializer = 初始值设定项（seed=0）。
- 第一个 BatchNorm 是规范化“通道”轴。
- 然后应用 ReLU 激活函数。这没有超参数。
主路径的第二个组成部分：
- 第二个 CONV2D 具有F2个形状 （f，f） 和步幅为 （1，1） 的过滤器。它的填充是“相同的”。使用 0 作为种子glorot_uniform kernel_initializer = 初始值设定项（seed=0）。
- 第二个 BatchNorm 是规范化“通道”轴。
- 然后应用 ReLU 激活函数。这没有超参数。
主路径的第三个组成部分：
- 第三个 CONV2D 具有F3个形状为 （1，1） 和步幅为 （1，1） 的过滤器。它的填充是“有效的”。使用 0 作为种子glorot_uniform kernel_initializer = 初始值设定项（seed=0）。
- 第三个 BatchNorm 是规范化“通道”轴。请注意，此组件中没有 ReLU 激活函数。
快捷方式路径：
- CONV2D 具有F3个形状为 （1，1） 和步幅为 （s，s） 的过滤器。它的填充是“有效的”。使用 0 作为种子glorot_uniform kernel_initializer = 初始值设定项（seed=0）。
- BatchNorm正在规范化“通道”轴。
最后一步：
- 快捷方式和主路径值相加。
- 然后应用 ReLU 激活函数。这没有超参数。
def convolutional_block(X, f, filters, s = 2, training=True, initializer=glorot_uniform): """ 图 4 中定义的卷积块的实现 Arguments: X -- 形状的输入张量（m、n_H_prev、n_W_prev、n_C_prev） f -- 整数，指定主路径中间 CONV 窗口的形状 filters -- python 整数列表，定义主路径的 CONV 层中的过滤器数量 s -- 整数，指定要使用的步幅 训练 -- True：在训练模式下行为 错误：在推理模式下行为 初始值设定项 -- 设置图层的初始权重。等于 Glorot 统一初始值设定项， 也称为泽维尔均匀初始值设定项。 Returns: X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C) """ # Retrieve Filters F1, F2, F3 = filters # Save the input value X_shortcut = X ##### MAIN PATH ##### # First component of main path glorot_uniform(seed=0) X = Conv2D(filters = F1, kernel_size = 1, strides = (s, s), padding='valid', kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training=training) X = Activation('relu')(X) ### START CODE HERE ## Second component of main path (≈3 lines) X = Conv2D(filters = F2, kernel_size = f,strides = (1, 1),padding='same',kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training=training) X = Activation('relu')(X) ## Third component of main path (≈2 lines) X = Conv2D(filters = F3, kernel_size = 1, strides = (1, 1), padding='valid', kernel_initializer = initializer(seed=0))(X) X = BatchNormalization(axis = 3)(X, training=training) ##### SHORTCUT PATH ##### (≈2 lines) X_shortcut = Conv2D(filters = F3, kernel_size = 1, strides = (s, s), padding='valid', kernel_initializer = initializer(seed=0))(X_shortcut) X_shortcut = BatchNormalization(axis = 3)(X_shortcut, training=training) ### END CODE HERE # Final step: Add shortcut value to main path (Use this order [X, X_shortcut]), and pass it through a RELU activation X = Add()([X, X_shortcut]) X = Activation('relu')(X) return X
我们测试一下：
from outputs import convolutional_block_output1, convolutional_block_output2 np.random.seed(1) #X = np.random.randn(3, 4, 4, 6).astype(np.float32) X1 = np.ones((1, 4, 4, 3)) * -1 X2 = np.ones((1, 4, 4, 3)) * 1 X3 = np.ones((1, 4, 4, 3)) * 3 X = np.concatenate((X1, X2, X3), axis = 0).astype(np.float32) A = convolutional_block(X, f = 2, filters = [2, 4, 6], training=False) assert type(A) == EagerTensor, "Use only tensorflow and keras functions" assert tuple(tf.shape(A).numpy()) == (3, 2, 2, 6), "Wrong shape." assert np.allclose(A.numpy(), convolutional_block_output1), "Wrong values when training=False." print(A[0]) B = convolutional_block(X, f = 2, filters = [2, 4, 6], training=True) assert np.allclose(B.numpy(), convolutional_block_output2), "Wrong values when training=True." print(' 33[92mAll tests passed!')
tf.Tensor( [[[0. 0.66683817 0. 0. 0.88853896 0.5274254 ] [0. 0.65053666 0. 0. 0.89592844 0.49965227]] [[0. 0.6312079 0. 0. 0.8636247 0.47643146] [0. 0.5688321 0. 0. 0.85534114 0.41709304]]], shape=(2, 2, 6), dtype=float32) All tests passed!
构建你的第一个残差网络（50层）
您现在拥有构建非常深入的 ResNet 所需的块。下图详细描述了该神经网络的架构。图中的“ID BLOCK”代表“身份块”，“ID BLOCK x3”表示您应该将 3 个恒等快块堆叠在一起。
零填充用 （3，3） 的填充填充输入
步骤一：
- 2D 卷积有 64 个形状为 （7，7） 的过滤器，并使用 （2，2） 的步幅。
- BatchNorm 应用于输入的“通道”轴。
- 最大池化使用 （3，3） 窗口和 （2，2） 步幅。
步骤二：
- 卷积块使用三组大小为 [64，64，256] 的过滤器，“f”为 3，“s” 为 1。
- 2 个身份块使用三组大小为 [64，64，256] 的过滤器，“f”为 3。
步骤三:
- 卷积块使用三组大小为 [128，128，512] 的过滤器，“f”为 3，“s” 为 2。
- 3 个身份块使用三组大小为 [128，128，512] 的过滤器，“f”为 3。
步骤四：
- 卷积块使用三组大小为 [256， 256， 1024] 的滤波器，“f” 为 3，“s” 为 2。
- 这 5 个身份块使用三组大小为 [256、256、1024] 的过滤器，“f”为 3。
步骤五：
- 卷积块使用三组大小为 [512， 512， 2048] 的滤波器，“f”为 3，“s” 为 2。
- 2 个身份块使用三组大小为 [512， 512， 2048] 的过滤器，“f”为 3。
二维平均池化使用形状为 （2，2） 的窗口。
“扁平化”层没有任何超参数。
全连接（密集）层使用 softmax 激活将其输入减少到类数。
def ResNet50(input_shape = (64, 64, 3), classes = 6): """ 流行的 ResNet50 架构的分阶段实现： CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> FLATTEN -> DENSE Arguments: input_shape -- shape of the images of the dataset classes -- integer, number of classes Returns: model -- a Model() instance in Keras """ # Define the input as a tensor with shape input_shape X_input = Input(input_shape) # Zero-Padding X = ZeroPadding2D((3, 3))(X_input) # Stage 1 X = Conv2D(64, (7, 7), strides = (2, 2), kernel_initializer = glorot_uniform(seed=0))(X) X = BatchNormalization(axis = 3)(X) X = Activation('relu')(X) X = MaxPooling2D((3, 3), strides=(2, 2))(X) # Stage 2 X = convolutional_block(X, f = 3, filters = [64, 64, 256], s = 1) X = identity_block(X, 3, [64, 64, 256]) X = identity_block(X, 3, [64, 64, 256]) ### START CODE HERE ## Stage 3 (≈4 lines) X = convolutional_block(X, f = 3, filters = [128,128,512], s = 2) X = identity_block(X, 3, [128,128,512]) X = identity_block(X, 3, [128,128,512]) X = identity_block(X, 3, [128,128,512]) ## Stage 4 (≈6 lines) X = convolutional_block(X, f = 3, filters = [256, 256, 1024], s = 2) X = identity_block(X, 3, [256, 256, 1024]) X = identity_block(X, 3, [256, 256, 1024]) X = identity_block(X, 3, [256, 256, 1024]) X = identity_block(X, 3, [256, 256, 1024]) X = identity_block(X, 3, [256, 256, 1024]) ## Stage 5 (≈3 lines) X = convolutional_block(X, f = 3, filters = [512, 512, 2048], s = 2) X = identity_block(X, 3, [512, 512, 2048]) X = identity_block(X, 3, [512, 512, 2048]) ## AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)" X = AveragePooling2D((2, 2))(X) ### END CODE HERE # output layer X = Flatten()(X) X = Dense(classes, activation='softmax', kernel_initializer = glorot_uniform(seed=0))(X) # Create model model = Model(inputs = X_input, outputs = X) return model
模型建立完成了，我们来看一下参数
model = ResNet50(input_shape = (64, 64, 3), classes = 6) print(model.summary())
Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 64, 64, 3)] 0 __________________________________________________________________________________________________ zero_padding2d (ZeroPadding2D) (None, 70, 70, 3) 0 input_1[0][0] __________________________________________________________________________________________________ conv2d_20 (Conv2D) (None, 32, 32, 64) 9472 zero_padding2d[0][0] __________________________________________________________________________________________________ batch_normalization_20 (BatchNo (None, 32, 32, 64) 256 conv2d_20[0][0] __________________________________________________________________________________________________ activation_18 (Activation) (None, 32, 32, 64) 0 batch_normalization_20[0][0] __________________________________________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 15, 15, 64) 0 activation_18[0][0] __________________________________________________________________________________________________ conv2d_21 (Conv2D) (None, 15, 15, 64) 4160 max_pooling2d[0][0] __________________________________________________________________________________________________ batch_normalization_21 (BatchNo (None, 15, 15, 64) 256 conv2d_21[0][0] __________________________________________________________________________________________________ activation_19 (Activation) (None, 15, 15, 64) 0 batch_normalization_21[0][0] __________________________________________________________________________________________________ conv2d_22 (Conv2D) (None, 15, 15, 64) 36928 activation_19[0][0] __________________________________________________________________________________________________ batch_normalization_22 (BatchNo (None, 15, 15, 64) 256 conv2d_22[0][0] __________________________________________________________________________________________________ activation_20 (Activation) (None, 15, 15, 64) 0 batch_normalization_22[0][0] __________________________________________________________________________________________________ conv2d_23 (Conv2D) (None, 15, 15, 256) 16640 activation_20[0][0] __________________________________________________________________________________________________ conv2d_24 (Conv2D) (None, 15, 15, 256) 16640 max_pooling2d[0][0] __________________________________________________________________________________________________ batch_normalization_23 (BatchNo (None, 15, 15, 256) 1024 conv2d_23[0][0] __________________________________________________________________________________________________ batch_normalization_24 (BatchNo (None, 15, 15, 256) 1024 conv2d_24[0][0] __________________________________________________________________________________________________ add_6 (Add) (None, 15, 15, 256) 0 batch_normalization_23[0][0] batch_normalization_24[0][0] __________________________________________________________________________________________________ activation_21 (Activation) (None, 15, 15, 256) 0 add_6[0][0] __________________________________________________________________________________________________ conv2d_25 (Conv2D) (None, 15, 15, 64) 16448 activation_21[0][0] __________________________________________________________________________________________________ batch_normalization_25 (BatchNo (None, 15, 15, 64) 256 conv2d_25[0][0] __________________________________________________________________________________________________ activation_22 (Activation) (None, 15, 15, 64) 0 batch_normalization_25[0][0] __________________________________________________________________________________________________ conv2d_26 (Conv2D) (None, 15, 15, 64) 36928 activation_22[0][0] __________________________________________________________________________________________________ batch_normalization_26 (BatchNo (None, 15, 15, 64) 256 conv2d_26[0][0] __________________________________________________________________________________________________ activation_23 (Activation) (None, 15, 15, 64) 0 batch_normalization_26[0][0] __________________________________________________________________________________________________ conv2d_27 (Conv2D) (None, 15, 15, 256) 16640 activation_23[0][0] __________________________________________________________________________________________________ batch_normalization_27 (BatchNo (None, 15, 15, 256) 1024 conv2d_27[0][0] __________________________________________________________________________________________________ add_7 (Add) (None, 15, 15, 256) 0 activation_21[0][0] batch_normalization_27[0][0] __________________________________________________________________________________________________ activation_24 (Activation) (None, 15, 15, 256) 0 add_7[0][0] __________________________________________________________________________________________________ conv2d_28 (Conv2D) (None, 15, 15, 64) 16448 activation_24[0][0] __________________________________________________________________________________________________ batch_normalization_28 (BatchNo (None, 15, 15, 64) 256 conv2d_28[0][0] __________________________________________________________________________________________________ activation_25 (Activation) (None, 15, 15, 64) 0 batch_normalization_28[0][0] __________________________________________________________________________________________________ conv2d_29 (Conv2D) (None, 15, 15, 64) 36928 activation_25[0][0] __________________________________________________________________________________________________ batch_normalization_29 (BatchNo (None, 15, 15, 64) 256 conv2d_29[0][0] __________________________________________________________________________________________________ activation_26 (Activation) (None, 15, 15, 64) 0 batch_normalization_29[0][0] __________________________________________________________________________________________________ conv2d_30 (Conv2D) (None, 15, 15, 256) 16640 activation_26[0][0] __________________________________________________________________________________________________ batch_normalization_30 (BatchNo (None, 15, 15, 256) 1024 conv2d_30[0][0] __________________________________________________________________________________________________ add_8 (Add) (None, 15, 15, 256) 0 activation_24[0][0] batch_normalization_30[0][0] __________________________________________________________________________________________________ activation_27 (Activation) (None, 15, 15, 256) 0 add_8[0][0] __________________________________________________________________________________________________ conv2d_31 (Conv2D) (None, 8, 8, 128) 32896 activation_27[0][0] __________________________________________________________________________________________________ batch_normalization_31 (BatchNo (None, 8, 8, 128) 512 conv2d_31[0][0] __________________________________________________________________________________________________ activation_28 (Activation) (None, 8, 8, 128) 0 batch_normalization_31[0][0] __________________________________________________________________________________________________ conv2d_32 (Conv2D) (None, 8, 8, 128) 147584 activation_28[0][0] __________________________________________________________________________________________________ batch_normalization_32 (BatchNo (None, 8, 8, 128) 512 conv2d_32[0][0] __________________________________________________________________________________________________ activation_29 (Activation) (None, 8, 8, 128) 0 batch_normalization_32[0][0] __________________________________________________________________________________________________ conv2d_33 (Conv2D) (None, 8, 8, 512) 66048 activation_29[0][0] __________________________________________________________________________________________________ conv2d_34 (Conv2D) (None, 8, 8, 512) 131584 activation_27[0][0] __________________________________________________________________________________________________ batch_normalization_33 (BatchNo (None, 8, 8, 512) 2048 conv2d_33[0][0] __________________________________________________________________________________________________ batch_normalization_34 (BatchNo (None, 8, 8, 512) 2048 conv2d_34[0][0] __________________________________________________________________________________________________ add_9 (Add) (None, 8, 8, 512) 0 batch_normalization_33[0][0] batch_normalization_34[0][0] __________________________________________________________________________________________________ activation_30 (Activation) (None, 8, 8, 512) 0 add_9[0][0] __________________________________________________________________________________________________ conv2d_35 (Conv2D) (None, 8, 8, 128) 65664 activation_30[0][0] __________________________________________________________________________________________________ batch_normalization_35 (BatchNo (None, 8, 8, 128) 512 conv2d_35[0][0] __________________________________________________________________________________________________ activation_31 (Activation) (None, 8, 8, 128) 0 batch_normalization_35[0][0] __________________________________________________________________________________________________ conv2d_36 (Conv2D) (None, 8, 8, 128) 147584 activation_31[0][0] __________________________________________________________________________________________________ batch_normalization_36 (BatchNo (None, 8, 8, 128) 512 conv2d_36[0][0] __________________________________________________________________________________________________ activation_32 (Activation) (None, 8, 8, 128) 0 batch_normalization_36[0][0] __________________________________________________________________________________________________ conv2d_37 (Conv2D) (None, 8, 8, 512) 66048 activation_32[0][0] __________________________________________________________________________________________________ batch_normalization_37 (BatchNo (None, 8, 8, 512) 2048 conv2d_37[0][0] __________________________________________________________________________________________________ add_10 (Add) (None, 8, 8, 512) 0 activation_30[0][0] batch_normalization_37[0][0] __________________________________________________________________________________________________ activation_33 (Activation) (None, 8, 8, 512) 0 add_10[0][0] __________________________________________________________________________________________________ conv2d_38 (Conv2D) (None, 8, 8, 128) 65664 activation_33[0][0] __________________________________________________________________________________________________ batch_normalization_38 (BatchNo (None, 8, 8, 128) 512 conv2d_38[0][0] __________________________________________________________________________________________________ activation_34 (Activation) (None, 8, 8, 128) 0 batch_normalization_38[0][0] __________________________________________________________________________________________________ conv2d_39 (Conv2D) (None, 8, 8, 128) 147584 activation_34[0][0] __________________________________________________________________________________________________ batch_normalization_39 (BatchNo (None, 8, 8, 128) 512 conv2d_39[0][0] __________________________________________________________________________________________________ activation_35 (Activation) (None, 8, 8, 128) 0 batch_normalization_39[0][0] __________________________________________________________________________________________________ conv2d_40 (Conv2D) (None, 8, 8, 512) 66048 activation_35[0][0] __________________________________________________________________________________________________ batch_normalization_40 (BatchNo (None, 8, 8, 512) 2048 conv2d_40[0][0] __________________________________________________________________________________________________ add_11 (Add) (None, 8, 8, 512) 0 activation_33[0][0] batch_normalization_40[0][0] __________________________________________________________________________________________________ activation_36 (Activation) (None, 8, 8, 512) 0 add_11[0][0] __________________________________________________________________________________________________ conv2d_41 (Conv2D) (None, 8, 8, 128) 65664 activation_36[0][0] __________________________________________________________________________________________________ batch_normalization_41 (BatchNo (None, 8, 8, 128) 512 conv2d_41[0][0] __________________________________________________________________________________________________ activation_37 (Activation) (None, 8, 8, 128) 0 batch_normalization_41[0][0] __________________________________________________________________________________________________ conv2d_42 (Conv2D) (None, 8, 8, 128) 147584 activation_37[0][0] __________________________________________________________________________________________________ batch_normalization_42 (BatchNo (None, 8, 8, 128) 512 conv2d_42[0][0] __________________________________________________________________________________________________ activation_38 (Activation) (None, 8, 8, 128) 0 batch_normalization_42[0][0] __________________________________________________________________________________________________ conv2d_43 (Conv2D) (None, 8, 8, 512) 66048 activation_38[0][0] __________________________________________________________________________________________________ batch_normalization_43 (BatchNo (None, 8, 8, 512) 2048 conv2d_43[0][0] __________________________________________________________________________________________________ add_12 (Add) (None, 8, 8, 512) 0 activation_36[0][0] batch_normalization_43[0][0] __________________________________________________________________________________________________ activation_39 (Activation) (None, 8, 8, 512) 0 add_12[0][0] __________________________________________________________________________________________________ conv2d_44 (Conv2D) (None, 4, 4, 256) 131328 activation_39[0][0] __________________________________________________________________________________________________ batch_normalization_44 (BatchNo (None, 4, 4, 256) 1024 conv2d_44[0][0] __________________________________________________________________________________________________ activation_40 (Activation) (None, 4, 4, 256) 0 batch_normalization_44[0][0] __________________________________________________________________________________________________ conv2d_45 (Conv2D) (None, 4, 4, 256) 590080 activation_40[0][0] __________________________________________________________________________________________________ batch_normalization_45 (BatchNo (None, 4, 4, 256) 1024 conv2d_45[0][0] __________________________________________________________________________________________________ activation_41 (Activation) (None, 4, 4, 256) 0 batch_normalization_45[0][0] __________________________________________________________________________________________________ conv2d_46 (Conv2D) (None, 4, 4, 1024) 263168 activation_41[0][0] __________________________________________________________________________________________________ conv2d_47 (Conv2D) (None, 4, 4, 1024) 525312 activation_39[0][0] __________________________________________________________________________________________________ batch_normalization_46 (BatchNo (None, 4, 4, 1024) 4096 conv2d_46[0][0] __________________________________________________________________________________________________ batch_normalization_47 (BatchNo (None, 4, 4, 1024) 4096 conv2d_47[0][0] __________________________________________________________________________________________________ add_13 (Add) (None, 4, 4, 1024) 0 batch_normalization_46[0][0] batch_normalization_47[0][0] __________________________________________________________________________________________________ activation_42 (Activation) (None, 4, 4, 1024) 0 add_13[0][0] __________________________________________________________________________________________________ conv2d_48 (Conv2D) (None, 4, 4, 256) 262400 activation_42[0][0] __________________________________________________________________________________________________ batch_normalization_48 (BatchNo (None, 4, 4, 256) 1024 conv2d_48[0][0] __________________________________________________________________________________________________ activation_43 (Activation) (None, 4, 4, 256) 0 batch_normalization_48[0][0] __________________________________________________________________________________________________ conv2d_49 (Conv2D) (None, 4, 4, 256) 590080 activation_43[0][0] __________________________________________________________________________________________________ batch_normalization_49 (BatchNo (None, 4, 4, 256) 1024 conv2d_49[0][0] __________________________________________________________________________________________________ activation_44 (Activation) (None, 4, 4, 256) 0 batch_normalization_49[0][0] __________________________________________________________________________________________________ conv2d_50 (Conv2D) (None, 4, 4, 1024) 263168 activation_44[0][0] __________________________________________________________________________________________________ batch_normalization_50 (BatchNo (None, 4, 4, 1024) 4096 conv2d_50[0][0] __________________________________________________________________________________________________ add_14 (Add) (None, 4, 4, 1024) 0 activation_42[0][0] batch_normalization_50[0][0] __________________________________________________________________________________________________ activation_45 (Activation) (None, 4, 4, 1024) 0 add_14[0][0] __________________________________________________________________________________________________ conv2d_51 (Conv2D) (None, 4, 4, 256) 262400 activation_45[0][0] __________________________________________________________________________________________________ batch_normalization_51 (BatchNo (None, 4, 4, 256) 1024 conv2d_51[0][0] __________________________________________________________________________________________________ activation_46 (Activation) (None, 4, 4, 256) 0 batch_normalization_51[0][0] __________________________________________________________________________________________________ conv2d_52 (Conv2D) (None, 4, 4, 256) 590080 activation_46[0][0] __________________________________________________________________________________________________ batch_normalization_52 (BatchNo (None, 4, 4, 256) 1024 conv2d_52[0][0] __________________________________________________________________________________________________ activation_47 (Activation) (None, 4, 4, 256) 0 batch_normalization_52[0][0] __________________________________________________________________________________________________ conv2d_53 (Conv2D) (None, 4, 4, 1024) 263168 activation_47[0][0] __________________________________________________________________________________________________ batch_normalization_53 (BatchNo (None, 4, 4, 1024) 4096 conv2d_53[0][0] __________________________________________________________________________________________________ add_15 (Add) (None, 4, 4, 1024) 0 activation_45[0][0] batch_normalization_53[0][0] __________________________________________________________________________________________________ activation_48 (Activation) (None, 4, 4, 1024) 0 add_15[0][0] __________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 4, 4, 256) 262400 activation_48[0][0] __________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 4, 4, 256) 1024 conv2d_54[0][0] __________________________________________________________________________________________________ activation_49 (Activation) (None, 4, 4, 256) 0 batch_normalization_54[0][0] __________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 4, 4, 256) 590080 activation_49[0][0] __________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 4, 4, 256) 1024 conv2d_55[0][0] __________________________________________________________________________________________________ activation_50 (Activation) (None, 4, 4, 256) 0 batch_normalization_55[0][0] __________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 4, 4, 1024) 263168 activation_50[0][0] __________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 4, 4, 1024) 4096 conv2d_56[0][0] __________________________________________________________________________________________________ add_16 (Add) (None, 4, 4, 1024) 0 activation_48[0][0] batch_normalization_56[0][0] __________________________________________________________________________________________________ activation_51 (Activation) (None, 4, 4, 1024) 0 add_16[0][0] __________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 4, 4, 256) 262400 activation_51[0][0] __________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 4, 4, 256) 1024 conv2d_57[0][0] __________________________________________________________________________________________________ activation_52 (Activation) (None, 4, 4, 256) 0 batch_normalization_57[0][0] __________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 4, 4, 256) 590080 activation_52[0][0] __________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 4, 4, 256) 1024 conv2d_58[0][0] __________________________________________________________________________________________________ activation_53 (Activation) (None, 4, 4, 256) 0 batch_normalization_58[0][0] __________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 4, 4, 1024) 263168 activation_53[0][0] __________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 4, 4, 1024) 4096 conv2d_59[0][0] __________________________________________________________________________________________________ add_17 (Add) (None, 4, 4, 1024) 0 activation_51[0][0] batch_normalization_59[0][0] __________________________________________________________________________________________________ activation_54 (Activation) (None, 4, 4, 1024) 0 add_17[0][0] __________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 4, 4, 256) 262400 activation_54[0][0] __________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 4, 4, 256) 1024 conv2d_60[0][0] __________________________________________________________________________________________________ activation_55 (Activation) (None, 4, 4, 256) 0 batch_normalization_60[0][0] __________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 4, 4, 256) 590080 activation_55[0][0] __________________________________________________________________________________________________ batch_normalization_61 (BatchNo (None, 4, 4, 256) 1024 conv2d_61[0][0] __________________________________________________________________________________________________ activation_56 (Activation) (None, 4, 4, 256) 0 batch_normalization_61[0][0] __________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 4, 4, 1024) 263168 activation_56[0][0] __________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 4, 4, 1024) 4096 conv2d_62[0][0] __________________________________________________________________________________________________ add_18 (Add) (None, 4, 4, 1024) 0 activation_54[0][0] batch_normalization_62[0][0] __________________________________________________________________________________________________ activation_57 (Activation) (None, 4, 4, 1024) 0 add_18[0][0] __________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 2, 2, 512) 524800 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 2, 2, 512) 2048 conv2d_63[0][0] __________________________________________________________________________________________________ activation_58 (Activation) (None, 2, 2, 512) 0 batch_normalization_63[0][0] __________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 2, 2, 512) 2359808 activation_58[0][0] __________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 2, 2, 512) 2048 conv2d_64[0][0] __________________________________________________________________________________________________ activation_59 (Activation) (None, 2, 2, 512) 0 batch_normalization_64[0][0] __________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 2, 2, 2048) 1050624 activation_59[0][0] __________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 2, 2, 2048) 2099200 activation_57[0][0] __________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 2, 2, 2048) 8192 conv2d_65[0][0] __________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 2, 2, 2048) 8192 conv2d_66[0][0] __________________________________________________________________________________________________ add_19 (Add) (None, 2, 2, 2048) 0 batch_normalization_65[0][0] batch_normalization_66[0][0] __________________________________________________________________________________________________ activation_60 (Activation) (None, 2, 2, 2048) 0 add_19[0][0] __________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 2, 2, 512) 1049088 activation_60[0][0] __________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 2, 2, 512) 2048 conv2d_67[0][0] __________________________________________________________________________________________________ activation_61 (Activation) (None, 2, 2, 512) 0 batch_normalization_67[0][0] __________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 2, 2, 512) 2359808 activation_61[0][0] __________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 2, 2, 512) 2048 conv2d_68[0][0] __________________________________________________________________________________________________ activation_62 (Activation) (None, 2, 2, 512) 0 batch_normalization_68[0][0] __________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 2, 2, 2048) 1050624 activation_62[0][0] __________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 2, 2, 2048) 8192 conv2d_69[0][0] __________________________________________________________________________________________________ add_20 (Add) (None, 2, 2, 2048) 0 activation_60[0][0] batch_normalization_69[0][0] __________________________________________________________________________________________________ activation_63 (Activation) (None, 2, 2, 2048) 0 add_20[0][0] __________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 2, 2, 512) 1049088 activation_63[0][0] __________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 2, 2, 512) 2048 conv2d_70[0][0] __________________________________________________________________________________________________ activation_64 (Activation) (None, 2, 2, 512) 0 batch_normalization_70[0][0] __________________________________________________________________________________________________ conv2d_71 (Conv2D) (None, 2, 2, 512) 2359808 activation_64[0][0] __________________________________________________________________________________________________ batch_normalization_71 (BatchNo (None, 2, 2, 512) 2048 conv2d_71[0][0] __________________________________________________________________________________________________ activation_65 (Activation) (None, 2, 2, 512) 0 batch_normalization_71[0][0] __________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 2, 2, 2048) 1050624 activation_65[0][0] __________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 2, 2, 2048) 8192 conv2d_72[0][0] __________________________________________________________________________________________________ add_21 (Add) (None, 2, 2, 2048) 0 activation_63[0][0] batch_normalization_72[0][0] __________________________________________________________________________________________________ activation_66 (Activation) (None, 2, 2, 2048) 0 add_21[0][0] __________________________________________________________________________________________________ average_pooling2d (AveragePooli (None, 1, 1, 2048) 0 activation_66[0][0] __________________________________________________________________________________________________ flatten (Flatten) (None, 2048) 0 average_pooling2d[0][0] __________________________________________________________________________________________________ dense (Dense) (None, 6) 12294 flatten[0][0] ================================================================================================== Total params: 23,600,006 Trainable params: 23,546,886 Non-trainable params: 53,120 __________________________________________________________________________________________________ None
from outputs import ResNet50_summary model = ResNet50(input_shape = (64, 64, 3), classes = 6)
实体化结束，下面我们对模型进行编译
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
接下来我们就是加载数据集并进行训练
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() # Normalize image vectors X_train = X_train_orig / 255. X_test = X_test_orig / 255. # Convert training and test labels to one hot matrices Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_test = convert_to_one_hot(Y_test_orig, 6).T print ("number of training examples = " + str(X_train.shape[0])) print ("number of test examples = " + str(X_test.shape[0])) print ("X_train shape: " + str(X_train.shape)) print ("Y_train shape: " + str(Y_train.shape)) print ("X_test shape: " + str(X_test.shape)) print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 1080 number of test examples = 120 X_train shape: (1080, 64, 64, 3) Y_train shape: (1080, 6) X_test shape: (120, 64, 64, 3) Y_test shape: (120, 6)
model.fit(X_train, Y_train, epochs = 10, batch_size = 32)
Epoch 1/10 34/34 [==============================] - 10s 129ms/step - loss: 2.2749 - accuracy: 0.4417 Epoch 2/10 34/34 [==============================] - 3s 92ms/step - loss: 1.0135 - accuracy: 0.6889 Epoch 3/10 34/34 [==============================] - 3s 92ms/step - loss: 0.3830 - accuracy: 0.8694 Epoch 4/10 34/34 [==============================] - 3s 91ms/step - loss: 0.2390 - accuracy: 0.9241 Epoch 5/10 34/34 [==============================] - 3s 91ms/step - loss: 0.1527 - accuracy: 0.9519 0s - loss: 0.1 Epoch 6/10 34/34 [==============================] - 3s 91ms/step - loss: 0.1050 - accuracy: 0.9648 Epoch 7/10 34/34 [==============================] - 3s 91ms/step - loss: 0.1824 - accuracy: 0.9444 Epoch 8/10 34/34 [==============================] - 3s 92ms/step - loss: 0.5906 - accuracy: 0.8074 Epoch 9/10 34/34 [==============================] - 3s 91ms/step - loss: 0.4195 - accuracy: 0.8676 Epoch 10/10 34/34 [==============================] - 3s 91ms/step - loss: 0.3377 - accuracy: 0.9194
我们对模型进行评估
preds = model.evaluate(X_test, Y_test) print ("Loss = " + str(preds[0])) print ("Test Accuracy = " + str(preds[1]))
4/4 [==============================] - 1s 31ms/step - loss: 0.4844 - accuracy: 0.8417 Loss = 0.4843602478504181 Test Accuracy = 0.8416666388511658
博主已经在手势数据集上训练了自己的RESNET50模型的权重，你可以使用下面的代码载并运行博主的训练模型，
pre_trained_model = tf.keras.models.load_model('resnet50.h5')
然后测试一下博主训练出来的权值：
preds = pre_trained_model.evaluate(X_test, Y_test) print ("Loss = " + str(preds[0])) print ("Test Accuracy = " + str(preds[1]))
使用自己的图片做测试
img_path = 'C:/Users/Style/Desktop/kun.png' img = image.load_img(img_path, target_size=(64, 64)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = x/255.0 print('Input image shape:', x.shape) imshow(img) prediction = model.predict(x) print("Class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = ", prediction) print("Class:", np.argmax(prediction))
Input image shape: (1, 64, 64, 3) Class prediction vector [p(0), p(1), p(2), p(3), p(4), p(5)] = [[0.01301673 0.8742848 0.00662233 0.05449386 0.02079306 0.03078919]] Class: 1
pre_trained_model.summary()
