AIDeep Learning

DeepLearning.ai作业:(4-2)-- 深度卷积网络实例探究（Deep convolutional models:case studies）

dl.ai, homework
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本周作业分为两部分，一部分是keras的基本使用，另一部分是ResNet的构建。

Part1: Keras – Tutorial

Keras是TensorFlow的高层封装，可以更高效的实现神经网络的搭建。

先导入库

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import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
import pydot
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from kt_utils import *

import keras.backend as K
K.set_image_data_format('channels_last')
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow

%matplotlib inline

构建模型

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def HappyModel(input_shape):
    """
    Implementation of the HappyModel.
    
    Arguments:
    input_shape -- shape of the images of the dataset

    Returns:
    model -- a Model() instance in Keras
    """
    
    ### START CODE HERE ###
    # Feel free to use the suggested outline in the text above to get started, and run through the whole
    # exercise (including the later portions of this notebook) once. The come back also try out other
    # network architectures as well. 
    X_input = Input(input_shape)
    X = ZeroPadding2D((3, 3))(X_input)
    X = Conv2D(32,(7,7),strides=(1,1),name="Conv0")(X)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((2, 2), name='max_pool')(X)
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)
    model = Model(inputs = X_input, outputs = X, name='HappyModel')
    
    
    ### END CODE HERE ###
    
    return model

然后实例化这个模型

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### START CODE HERE ### (1 line)
happyModel = HappyModel(X_train.shape[1:])
### END CODE HERE ###

进行优化器和loss的选择

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### START CODE HERE ### (1 line)
happyModel.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])
### END CODE HERE ###

训练

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### START CODE HERE ### (1 line)
happyModel.fit(x=X_train,y = Y_train,epochs=10,batch_size=32)
### END CODE HERE ###

预测：

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### START CODE HERE ### (1 line)
preds = happyModel.evaluate(X_test,Y_test)
### END CODE HERE ###
print()
print ("Loss = " + str(preds[0]))
print ("Test Accuracy = " + str(preds[1]))

可以用summary()来看看详细信息：

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happyModel.summary()
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_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 64, 64, 3)         0         
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 70, 70, 3)         0         
_________________________________________________________________
Conv0 (Conv2D)               (None, 64, 64, 32)        4736      
_________________________________________________________________
bn0 (BatchNormalization)     (None, 64, 64, 32)        128       
_________________________________________________________________
activation_1 (Activation)    (None, 64, 64, 32)        0         
_________________________________________________________________
max_pool (MaxPooling2D)      (None, 32, 32, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 32768)             0         
_________________________________________________________________
fc (Dense)                   (None, 1)                 32769     
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64
_________________________________________________________________

用plot_model()来得到详细的graph

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plot_model(happyModel, to_file='HappyModel.png')
SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))

Part2: Residual Networks

主要有两个步骤：

  • 构建基本的ResNet的块
  • 将块放到一起，变成一个网络，来做图像分类

1 - The problem of very deep neural networks

这一部分非常深的神经网络的一些问题，主要是参数会变得很小或者爆炸，这样子训练的时候就会收敛的很慢，因此，用残差网络可以有效的改善这个问题。

2 - Building a Residual Network

根据输入输入的维度不同，分为两种块：

1. identity block（一致块）

可以看到，identity block的前后两端维度是一致的，可以直接相加。

在这里我们实现了一个跳跃三层的块。

基本结构是:

First component of main path:

  • The first CONV2D has F1F1 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be conv_name_base + '2a'. Use 0 as the seed for the random initialization.
  • The first BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2a'.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

Second component of main path:

  • The second CONV2D has F2F2 filters of shape (f,f)(f,f) and a stride of (1,1). Its padding is “same” and its name should be conv_name_base + '2b'. Use 0 as the seed for the random initialization.
  • The second BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2b'.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

Third component of main path:

  • The third CONV2D has F3F3 filters of shape (1,1) and a stride of (1,1). Its padding is “valid” and its name should be conv_name_base + '2c'. Use 0 as the seed for the random initialization.
  • The third BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2c'. Note that there is no ReLU activation function in this component.

Final step:

  • The shortcut and the input are added together.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

注意在跳跃相加部分要用函数keras的函授Add()，不能用加号，不然会出错。

这里f是卷积核的大小，filters是这三层卷积层的深度的list，stage指的是哪一大层的网络，用来取名字的，后面有用，block是在stage下的某一层的网络，用a,b,c,d等字母表示。

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# GRADED FUNCTION: identity_block

def identity_block(X, f, filters, stage, block):
    """
    Implementation of the identity block as defined in Figure 3
    
    Arguments:
    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
    f -- integer, specifying the shape of the middle CONV's window for the main path
    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
    stage -- integer, used to name the layers, depending on their position in the network
    block -- string/character, used to name the layers, depending on their position in the network
    
    Returns:
    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)
    """
    
    # defining name basis
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # 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
    
    # First component of main path
    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)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)
    X = Activation('relu')(X)
    
    ### START CODE HERE ###
    
    # Second component of main path (≈3 lines)
    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)

    # Third component of main path (≈2 lines)
    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)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    X = Add()([X,X_shortcut])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

2. The convolutional block(卷积块)

当两端的维度不一致时，可以加一个卷积核来转化维度，这时候没有激活函数。

First component of main path:

  • The first CONV2D has F1F1 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be conv_name_base + '2a'.
  • The first BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2a'.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

Second component of main path:

  • The second CONV2D has F2F2 filters of (f,f) and a stride of (1,1). Its padding is “same” and it’s name should be conv_name_base + '2b'.
  • The second BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2b'.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

Third component of main path:

  • The third CONV2D has F3F3 filters of (1,1) and a stride of (1,1). Its padding is “valid” and it’s name should be conv_name_base + '2c'.
  • The third BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '2c'. Note that there is no ReLU activation function in this component.

Shortcut path:

  • The CONV2D has F3F3 filters of shape (1,1) and a stride of (s,s). Its padding is “valid” and its name should be conv_name_base + '1'.
  • The BatchNorm is normalizing the channels axis. Its name should be bn_name_base + '1'.

Final step:

  • The shortcut and the main path values are added together.
  • Then apply the ReLU activation function. This has no name and no hyperparameters.

这里参数新增了s是stride每一步数

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def convolutional_block(X, f, filters, stage, block, s = 2):
    """
    Implementation of the convolutional block as defined in Figure 4
    
    Arguments:
    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)
    f -- integer, specifying the shape of the middle CONV's window for the main path
    filters -- python list of integers, defining the number of filters in the CONV layers of the main path
    stage -- integer, used to name the layers, depending on their position in the network
    block -- string/character, used to name the layers, depending on their position in the network
    s -- Integer, specifying the stride to be used
    
    Returns:
    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)
    """
    
    # defining name basis
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'
    
    # Retrieve Filters
    F1, F2, F3 = filters
    
    # Save the input value
    X_shortcut = X


    ##### MAIN PATH #####
    # First component of main path 
    X = Conv2D(F1, (1, 1), strides = (s,s), 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)
    
    ### START CODE HERE ###

    # Second component of main path (≈3 lines)
    X = Conv2D(F2, (f, f), strides = (1,1), name = conv_name_base + '2b', padding = 'same', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)
    X = Activation('relu')(X)

    # Third component of main path (≈2 lines)
    X = Conv2D(F3, (1, 1), strides = (1,1), name = conv_name_base + '2c', padding = 'valid', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)

    ##### SHORTCUT PATH #### (≈2 lines)
    X_shortcut = Conv2D(F3, (1, 1), strides = (s,s), name = conv_name_base + '1', padding = 'valid', kernel_initializer = glorot_uniform(seed=0))(X_shortcut)
    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)

    # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines)
    X = Add()([X,X_shortcut])
    X = Activation('relu')(X)
    
    ### END CODE HERE ###
    
    return X

3 - Building your first ResNet model (50 layers)

构建一个50层的网络，分为5块，结构如下：

The details of this ResNet-50 model are:

  • Zero-padding pads the input with a pad of (3,3)
  • Stage 1:
    • The 2D Convolution has 64 filters of shape (7,7) and uses a stride of (2,2). Its name is “conv1”.
    • BatchNorm is applied to the channels axis of the input.
    • MaxPooling uses a (3,3) window and a (2,2) stride.
  • Stage 2:
    • The convolutional block uses three set of filters of size [64,64,256], “f” is 3, “s” is 1 and the block is “a”.
    • The 2 identity blocks use three set of filters of size [64,64,256], “f” is 3 and the blocks are “b” and “c”.
  • Stage 3:
    • The convolutional block uses three set of filters of size [128,128,512], “f” is 3, “s” is 2 and the block is “a”.
    • The 3 identity blocks use three set of filters of size [128,128,512], “f” is 3 and the blocks are “b”, “c” and “d”.
  • Stage 4:
    • The convolutional block uses three set of filters of size [256, 256, 1024], “f” is 3, “s” is 2 and the block is “a”.
    • The 5 identity blocks use three set of filters of size [256, 256, 1024], “f” is 3 and the blocks are “b”, “c”, “d”, “e” and “f”.
  • Stage 5:
    • The convolutional block uses three set of filters of size [512, 512, 2048], “f” is 3, “s” is 2 and the block is “a”.
    • The 2 identity blocks use three set of filters of size [512, 512, 2048], “f” is 3 and the blocks are “b” and “c”.
  • The 2D Average Pooling uses a window of shape (2,2) and its name is “avg_pool”.
  • The flatten doesn’t have any hyperparameters or name.
  • The Fully Connected (Dense) layer reduces its input to the number of classes using a softmax activation. Its name should be 'fc' + str(classes).

Exercise: Implement the ResNet with 50 layers described in the figure above. We have implemented Stages 1 and 2. Please implement the rest. (The syntax for implementing Stages 3-5 should be quite similar to that of Stage 2.) Make sure you follow the naming convention in the text above.

You’ll need to use this function:

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# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):
    """
    Implementation of the popular ResNet50 the following architecture:
    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3
    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER

    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), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)
    X = BatchNormalization(axis = 3, name = 'bn_conv1')(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], stage = 2, block='a', s = 1)
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')


    ### START CODE HERE ###

    # Stage 3 (≈4 lines)

    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')



    # Stage 4 (≈6 lines)
    X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')


    # Stage 5 (≈3 lines)
    X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)
    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')
    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')



    # AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
    X = AveragePooling2D(pool_size=(2,2),strides=(1,1),padding='valid')(X)

    
    ### END CODE HERE ###

    # output layer
    X = Flatten()(X)
    X = Dense(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
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