吴恩达深度学习课程deeplearning.ai课程作业：Class 4 Week 2 Keras - Tutorial - Happy House

吴恩达deeplearning.ai课程作业，自己写的答案。
补充说明：
1. 评论中总有人问为什么直接复制这些notebook运行不了？请不要直接复制粘贴，不可能运行通过的，这个只是notebook中我们要自己写的那部分，要正确运行还需要其他py文件，请自己到GitHub上下载完整的。这里的部分仅仅是参考用的，建议还是自己按照提示一点一点写，如果实在卡住了再看答案。个人觉得这样才是正确的学习方法，况且作业也不算难。
2. 关于评论中有人说我是抄袭，注释还没别人详细，复制下来还运行不过。答复是：做伸手党之前，请先搞清这个作业是干什么的。大家都是从GitHub上下载原始的作业，然后根据代码前面的提示（通常会指定函数和公式）来编写代码，而且后面还有expected output供你比对，如果程序正确，结果一般来说是一样的。请不要无脑喷，说什么跟别人的答案一样的。说到底，我们要做的就是，看他的文字部分，根据提示在代码中加入部分自己的代码。我们自己要写的部分只有那么一小部分代码。
3. 由于实在很反感无脑喷子，故禁止了下面的评论功能，请见谅。如果有问题，请私信我，在力所能及的范围内会尽量帮忙。

Keras tutorial - the Happy House

Welcome to the first assignment of week 2. In this assignment, you will:
1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK.
2. See how you can in a couple of hours build a deep learning algorithm.

Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Being able to go from idea to result with the least possible delay is key to finding good models. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. That being said, Keras will work fine for many common models.

In this exercise, you’ll work on the “Happy House” problem, which we’ll explain below. Let’s load the required packages and solve the problem of the Happy House!

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

Using TensorFlow backend.

Note: As you can see, we’ve imported a lot of functions from Keras. You can use them easily just by calling them directly in the notebook. Ex: X = Input(...) or X = ZeroPadding2D(...).

1 - The Happy House

For your next vacation, you decided to spend a week with five of your friends from school. It is a very convenient house with many things to do nearby. But the most important benefit is that everybody has commited to be happy when they are in the house. So anyone wanting to enter the house must prove their current state of happiness.

Figure 1 : the Happy House

As a deep learning expert, to make sure the “Happy” rule is strictly applied, you are going to build an algorithm which that uses pictures from the front door camera to check if the person is happy or not. The door should open only if the person is happy.

You have gathered pictures of your friends and yourself, taken by the front-door camera. The dataset is labbeled.

Run the following code to normalize the dataset and learn about its shapes.

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.

# Reshape
Y_train = Y_train_orig.T
Y_test = Y_test_orig.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 = 600
number of test examples = 150
X_train shape: (600, 64, 64, 3)
Y_train shape: (600, 1)
X_test shape: (150, 64, 64, 3)
Y_test shape: (150, 1)

Details of the “Happy” dataset:
- Images are of shape (64,64,3)
- Training: 600 pictures
- Test: 150 pictures

It is now time to solve the “Happy” Challenge.

2 - Building a model in Keras

Keras is very good for rapid prototyping. In just a short time you will be able to build a model that achieves outstanding results.

Here is an example of a model in Keras:

def model(input_shape):
    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!
    X_input = Input(input_shape)

    # Zero-Padding: pads the border of X_input with zeroes
    X = ZeroPadding2D((3, 3))(X_input)

    # CONV -> BN -> RELU Block applied to X
    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)
    X = Activation('relu')(X)

    # MAXPOOL
    X = MaxPooling2D((2, 2), name='max_pool')(X)

    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)

    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.
    model = Model(inputs = X_input, outputs = X, name='HappyModel')

    return model

Note that Keras uses a different convention with variable names than we’ve previously used with numpy and TensorFlow. In particular, rather than creating and assigning a new variable on each step of forward propagation such as X, Z1, A1, Z2, A2, etc. for the computations for the different layers, in Keras code each line above just reassigns X to a new value using X = .... In other words, during each step of forward propagation, we are just writing the latest value in the commputation into the same variable X. The only exception was X_input, which we kept separate and did not overwrite, since we needed it at the end to create the Keras model instance (model = Model(inputs = X_input, ...) above).

Exercise: Implement a HappyModel(). This assignment is more open-ended than most. We suggest that you start by implementing a model using the architecture we suggest, and run through the rest of this assignment using that as your initial model. But after that, come back and take initiative to try out other model architectures. For example, you might take inspiration from the model above, but then vary the network architecture and hyperparameters however you wish. You can also use other functions such as AveragePooling2D(), GlobalMaxPooling2D(), Dropout().

Note: You have to be careful with your data’s shapes. Use what you’ve learned in the videos to make sure your convolutional, pooling and fully-connected layers are adapted to the volumes you’re applying it to.

# GRADED FUNCTION: HappyModel

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. 

    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!
    X_Input = Input(input_shape)

    # Zero-Padding: pads the border of X_input with zeroes
#     print(input_shape)
#     print(X_Input.shape)
    X = ZeroPadding2D((3, 3))(X_Input)

    # CONV -> BN -> RELU Block applied to X
    X = Conv2D(32, (7,7), strides=(1,1), name='conv0')(X)
    X = BatchNormalization(axis=3, name='bn0')(X)
    X = Activation('relu')(X)

    # MAXPOOL
    X = MaxPooling2D((2,2), name='max_pool')(X)

    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED
    X = Flatten()(X)
    X = Dense(1, activation='sigmoid', name='fc')(X)

    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.
    model = Model(inputs = X_Input, outputs = X, name='HappyModel')

    ### END CODE HERE ###

    return model

You have now built a function to describe your model. To train and test this model, there are four steps in Keras:
1. Create the model by calling the function above
2. Compile the model by calling model.compile(optimizer = "...", loss = "...", metrics = ["accuracy"])
3. Train the model on train data by calling model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)
4. Test the model on test data by calling model.evaluate(x = ..., y = ...)

If you want to know more about model.compile(), model.fit(), model.evaluate() and their arguments, refer to the official Keras documentation.

Exercise: Implement step 1, i.e. create the model.

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

Exercise: Implement step 2, i.e. compile the model to configure the learning process. Choose the 3 arguments of compile() wisely. Hint: the Happy Challenge is a binary classification problem.

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

Exercise: Implement step 3, i.e. train the model. Choose the number of epochs and the batch size.

### START CODE HERE ### (1 line)
happyModel.fit(x=X_train, y=Y_train, epochs=50, batch_size=64)
### END CODE HERE ###

Epoch 1/50
600/600 [==============================] - 18s 29ms/step - loss: 1.5964 - acc: 0.5800
Epoch 2/50
600/600 [==============================] - 17s 29ms/step - loss: 0.5761 - acc: 0.7517
Epoch 3/50
600/600 [==============================] - 17s 29ms/step - loss: 0.3519 - acc: 0.8500
Epoch 4/50
600/600 [==============================] - 17s 29ms/step - loss: 0.1496 - acc: 0.9433
Epoch 5/50
600/600 [==============================] - 17s 29ms/step - loss: 0.1191 - acc: 0.9633
Epoch 6/50
600/600 [==============================] - 17s 29ms/step - loss: 0.1326 - acc: 0.9533
Epoch 7/50
600/600 [==============================] - 17s 29ms/step - loss: 0.1102 - acc: 0.9600
Epoch 8/50
600/600 [==============================] - 17s 29ms/step - loss: 0.1211 - acc: 0.9517
Epoch 9/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0690 - acc: 0.9817
Epoch 10/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0737 - acc: 0.9817
Epoch 11/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0627 - acc: 0.9850
Epoch 12/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0693 - acc: 0.9817
Epoch 13/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0547 - acc: 0.9800
Epoch 14/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0466 - acc: 0.9900
Epoch 15/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0367 - acc: 0.9933
Epoch 16/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0366 - acc: 0.9917
Epoch 17/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0482 - acc: 0.9817
Epoch 18/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0582 - acc: 0.9800
Epoch 19/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0541 - acc: 0.9833
Epoch 20/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0373 - acc: 0.9900
Epoch 21/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0286 - acc: 0.9933
Epoch 22/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0293 - acc: 0.9917
Epoch 23/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0273 - acc: 0.9933
Epoch 24/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0215 - acc: 0.9950
Epoch 25/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0204 - acc: 0.9950
Epoch 26/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0168 - acc: 0.9950
Epoch 27/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0162 - acc: 0.9983
Epoch 28/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0167 - acc: 0.9967
Epoch 29/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0184 - acc: 0.9950
Epoch 30/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0211 - acc: 0.9983
Epoch 31/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0150 - acc: 0.9967
Epoch 32/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0143 - acc: 0.9967
Epoch 33/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0147 - acc: 0.9983
Epoch 34/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0112 - acc: 0.9983
Epoch 35/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0115 - acc: 0.9950
Epoch 36/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0129 - acc: 0.9950
Epoch 37/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0120 - acc: 0.9950
Epoch 38/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0125 - acc: 0.9967
Epoch 39/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0191 - acc: 0.9917
Epoch 40/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0131 - acc: 0.9983
Epoch 41/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0149 - acc: 0.9983
Epoch 42/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0189 - acc: 0.9950
Epoch 43/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0113 - acc: 0.9983
Epoch 44/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0089 - acc: 0.9983
Epoch 45/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0148 - acc: 0.9933
Epoch 46/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0100 - acc: 0.9983
Epoch 47/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0103 - acc: 0.9983
Epoch 48/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0064 - acc: 1.0000
Epoch 49/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0059 - acc: 0.9983
Epoch 50/50
600/600 [==============================] - 17s 29ms/step - loss: 0.0059 - acc: 0.9983

注：这里的训练我采用的epochs总共迭代50次，每个batch的大小事64个样本。这都算是比较大的了，只是为了看到什么时候训练开始变得平缓甚至过拟合。从训练中的结果可以看出，当训练的accuracy达到99.5%以上之后，基本就只是在99.5%上下浮动了。

Note that if you run fit() again, the model will continue to train with the parameters it has already learnt instead of reinitializing them.

Exercise: Implement step 4, i.e. test/evaluate the model.

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

150/150 [==============================] - 2s 15ms/step

Loss = 0.0938649992148
Test Accuracy = 0.953333330949

If your happyModel() function worked, you should have observed much better than random-guessing (50%) accuracy on the train and test sets. To pass this assignment, you have to get at least 75% accuracy.

To give you a point of comparison, our model gets around 95% test accuracy in 40 epochs (and 99% train accuracy) with a mini batch size of 16 and “adam” optimizer. But our model gets decent accuracy after just 2-5 epochs, so if you’re comparing different models you can also train a variety of models on just a few epochs and see how they compare.

If you have not yet achieved 75% accuracy, here’re some things you can play around with to try to achieve it:

• Try using blocks of CONV->BATCHNORM->RELU such as:

X = Conv2D(32, (3, 3), strides = (1, 1), name = 'conv0')(X)
X = BatchNormalization(axis = 3, name = 'bn0')(X)
X = Activation('relu')(X)

until your height and width dimensions are quite low and your number of channels quite large (≈32 for example). You are encoding useful information in a volume with a lot of channels. You can then flatten the volume and use a fully-connected layer.
- You can use MAXPOOL after such blocks. It will help you lower the dimension in height and width.
- Change your optimizer. We find Adam works well.
- If the model is struggling to run and you get memory issues, lower your batch_size (12 is usually a good compromise)
- Run on more epochs, until you see the train accuracy plateauing.

Even if you have achieved 75% accuracy, please feel free to keep playing with your model to try to get even better results.

Note: If you perform hyperparameter tuning on your model, the test set actually becomes a dev set, and your model might end up overfitting to the test (dev) set. But just for the purpose of this assignment, we won’t worry about that here.

3 - Conclusion

Congratulations, you have solved the Happy House challenge!

Now, you just need to link this model to the front-door camera of your house. We unfortunately won’t go into the details of how to do that here.

What we would like you to remember from this assignment:
- Keras is a tool we recommend for rapid prototyping. It allows you to quickly try out different model architectures. Are there any applications of deep learning to your daily life that you’d like to implement using Keras?
- Remember how to code a model in Keras and the four steps leading to the evaluation of your model on the test set. Create->Compile->Fit/Train->Evaluate/Test.

4 - Test with your own image (Optional)

Congratulations on finishing this assignment. You can now take a picture of your face and see if you could enter the Happy House. To do that:
1. Click on “File” in the upper bar of this notebook, then click “Open” to go on your Coursera Hub.
2. Add your image to this Jupyter Notebook’s directory, in the “images” folder
3. Write your image’s name in the following code
4. Run the code and check if the algorithm is right (0 is unhappy, 1 is happy)!

The training/test sets were quite similar; for example, all the pictures were taken against the same background (since a front door camera is always mounted in the same position). This makes the problem easier, but a model trained on this data may or may not work on your own data. But feel free to give it a try!

### START CODE HERE ###
img_path = 'images/my_image.jpg'
### END CODE HERE ###
img = image.load_img(img_path, target_size=(64, 64))
imshow(img)

x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

print(happyModel.predict(x))

[[ 0.]]

5 - Other useful functions in Keras (Optional)

Two other basic features of Keras that you’ll find useful are:
- model.summary(): prints the details of your layers in a table with the sizes of its inputs/outputs
- plot_model(): plots your graph in a nice layout. You can even save it as “.png” using SVG() if you’d like to share it on social media ;). It is saved in “File” then “Open…” in the upper bar of the notebook.

Run the following code.

happyModel.summary()

_________________________________________________________________
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
_________________________________________________________________

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

如果想要保存/读取keras的模型，可以使用下面的方法实现：

# save the model
happyModel.save('my_model_v1.h5')

# load the model
from keras.models import load_model
model = load_model('my_model_v1.h5')
model.summary()

_________________________________________________________________
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
_________________________________________________________________