DeepLearning.ai深度学习课程笔记
  • Introduction
  • 第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)
    • 第一周：深度学习引言(Introduction to Deep Learning)
      • 1.1 神经网络的监督学习(Supervised Learning with Neural Networks)
      • 1.2 为什么神经网络会流行？(Why is Deep Learning taking off?)
    • 第二周：神经网络的编程基础(Basics of Neural Network programming)
      • 2.1 二分类(Binary Classification)
      • 2.2 逻辑回归(Logistic Regression)
      • 2.3 逻辑回归的代价函数（Logistic Regression Cost Function）
      • 2.4 逻辑回归的梯度下降（Logistic Regression Gradient Descent）
      • 2.5 梯度下降的例子(Gradient Descent on m Examples)
      • 2.6 向量化 logistic 回归的梯度输出（Vectorizing Logistic Regression’s Gradient Output）
      • 2.7 （选修）logistic 损失函数的解释（Explanation of logistic regression cost function ）
      • Logistic Regression with a Neural Network mindset 代码
      • lr_utils.py
    • 第三周：浅层神经网络(Shallow neural networks)
      • 3.1 神经网络概述（Neural Network Overview）
      • 3.2 神经网络的表示（Neural Network Representation ）
      • 3.3 计算一个神经网络的输出（Computing a Neural Network's output ）
      • 3.4 多样本向量化（Vectorizing across multiple examples ）
      • 3.5 激活函数（Activation functions）
      • 3.6 为什么需要（ 非线性激活函数？（why need a nonlinear activation function?）
      • 3.7 激活函数的导数（Derivatives of activation functions ）
      • 3.8 神经网络的梯度下降（Gradient descent for neural networks）
      • 3.9 （选修）直观理解反向传播（Backpropagation intuition ）
      • 3.10 随机初始化（Random+Initialization）
      • Planar data classification with one hidden layer
      • planar_utils.py
      • testCases.py
    • 第四周：深层神经网络(Deep Neural Networks)
      • 4.1 深层神经网络（Deep L-layer neural network）
      • 4.2 前向传播和反向传播（Forward and backward propagation）
      • 4.3 深层网络中的前向传播（Forward propagation in a Deep Network ）
      • 4.4 为什么使用深层表示？（Why deep representations?）
      • 4.5 搭建神经网络块（Building blocks of deep neural networks）
      • 4.6 参数 VS 超参数（Parameters vs Hyperparameters）
      • Building your Deep Neural Network Step by Step
      • dnn_utils.py
      • testCases.py
      • Deep Neural Network Application
      • dnn_app_utils.py
  • 第二门课 改善深层神经网络：超参数调试、 正 则 化 以 及 优 化 (Improving Deep Neural Networks:Hyperparameter tuning, Regulariza
    • 第二门课 改善深层神经网络：超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and
      • 第一周：深度学习的实用层面(Practical aspects of Deep Learning)
        • 1.1 训练，验证，测试集（Train / Dev / Test sets）
        • 1.2 偏差，方差（Bias /Variance）
        • 1.3 机器学习基础（Basic Recipe for Machine Learning）
        • 1.4 正则化（Regularization）
        • 1.5 为什么正则化有利于预防过拟合呢？（Why regularization reduces overfitting?）
        • 1.6 dropout 正则化（Dropout Regularization）
        • 1.7 理解 dropout（Understanding Dropout）
        • 1.8 其他正则化方法（Other regularization methods）
        • 1.9 归一化输入（Normalizing inputs）
        • 1.10 梯度消失/梯度爆炸（Vanishing / Exploding gradients）
        • 1.11 神经网络的权重初始化（Weight Initialization for Deep Networks）
        • 1.12 梯度的数值逼近（Numerical approximation of gradients）
        • 1.13 梯度检验（Gradient checking）
        • 1.14 梯度检验应用的注意事项（Gradient Checking Implementation Notes）
        • Initialization
        • Gradient Checking
        • Regularization
        • reg_utils.py
        • testCases.py
      • 第二周：优化算法 (Optimization algorithms)
        • 2.1 Mini-batch 梯度下降（Mini-batch gradient descent）
        • 2.2 理解 mini-batch 梯度下降法（Understanding mini-batch gradient descent）
        • 2.3 指数加权平均数（Exponentially weighted averages）
        • 2.4 理解指数加权平均数（Understanding exponentially weighted averages ）
        • 2.5 指 数 加 权 平 均 的 偏 差 修 正 （ Bias correction in exponentially weighted averages ）
        • 2.6 动量梯度下降法（Gradient descent with Momentum ）
        • 2.7 RMSprop( root mean square prop)
        • 2.8 Adam 优化算法(Adam optimization algorithm)
        • 2.9 学习率衰减(Learning rate decay)
        • 2.10 局部最优的问题(The problem of local optima)
        • Optimization
        • opt_utils.py
        • testCases.py
      • 第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 （Hyperparameter tuning）
        • 3.1 调试处理（Tuning process）
        • 3.2 为超参数选择合适的范围（Using an appropriate scale to pick hyperparameters）
        • 3.3 超参数训练的实践： Pandas VS Caviar（Hyperparameters tuning in practice: Pandas vs. Caviar）
        • 3.4 归一化网络的激活函数（ Normalizing activations in a network）
        • 3.5 将 Batch Norm 拟合进神经网络（Fitting Batch Norm into a neural network）
        • 3.6 Batch Norm 为什么奏效？（Why does Batch Norm work?）
        • 3.7 测试时的 Batch Norm（Batch Norm at test time）
        • 3.8 Softmax 回归（Softmax regression）
        • 3.9 训练一个 Softmax 分类器（Training a Softmax classifier）
        • tensorflow tutorial
        • improv_utils.py
        • tf_utils.py
  • 第三门课 结构化机器学习项目（Structuring Machine Learning Projects）
    • 第三门课 结构化机器学习项目（Structuring Machine Learning Projects）
      • 第一周 机器学习（ML）策略（1）（ML strategy（1））
        • 1.1 为什么是 ML 策略？（Why ML Strategy?）
        • 1.2 正交化（Orthogonalization）
        • 1.3 单一数字评估指标（Single number evaluation metric）
        • 1.4 满足和优化指标（Satisficing and optimizing metrics）
        • 1.5 训练/开发/测试集划分（Train/dev/test distributions）
        • 1.6 开发集和测试集的大小（Size of dev and test sets）
        • 1.7 什么时候该改变开发/测试集和指标？（When to change dev/test sets and metrics）
        • 1.8 为什么是人的表现？（ Why human-level performance?）
        • 1.9 可避免偏差（Avoidable bias）
        • 1.10 理解人的表现（Understanding human-level performance）
        • 1.11 超过人的表现（Surpassing human- level performance）
        • 1.12 改善你的模型的表现（Improving your model performance）
      • 第二周：机器学习策略（2）(ML Strategy (2))
        • 2.1 进行误差分析（Carrying out error analysis）
        • 2.2 清楚标注错误的数据（Cleaning up Incorrectly labeled data）
        • 2.3 快速搭建你的第一个系统，并进行迭代（Build your first system quickly, then iterate）
        • 2.4 在不同的划分上进行训练并测试（Training and testing on different distributions）
        • 2.5 不匹配数据划分的偏差和方差（Bias and Variance with mismatched data distributions）
        • 2.6 定位数据不匹配（Addressing data mismatch）
        • 2.7 迁移学习（Transfer learning）
        • 2.8 多任务学习（Multi-task learning）
        • 2.9 什么是端到端的深度学习？（What is end-to-end deep learning?）
        • 2.10 是否要使用端到端的深度学习？（Whether to use end-to-end learning?）
  • 第四门课 卷积神经网络（Convolutional Neural Networks）
    • 第四门课 卷积神经网络（Convolutional Neural Networks）
      • 第一周 卷积神经网络（Foundations of Convolutional Neural Networks）
        • 1.1 计算机视觉（Computer vision）
        • 1.2 边缘检测示例（Edge detection example）
        • 1.3 更多边缘检测内容（More edge detection）
        • 1.4 Padding
        • 1.5 卷积步长（Strided convolutions）
        • 1.6 三维卷积（Convolutions over volumes）
        • 1.7 单层卷积网络（One layer of a convolutional network）
        • 1.8 简单卷积网络示例（A simple convolution network example）
        • 1.9 池化层（Pooling layers）
        • 1.10 卷积神经网络示例（Convolutional neural network example）
        • 1.11 为什么使用卷积？（Why convolutions?）
        • Convolution model Step by Step
        • Convolutional Neural Networks: Application
        • cnn_utils
      • 第二周 深度卷积网络：实例探究（Deep convolutional models: case studies）
        • 2.1 经典网络（Classic networks）
        • 2.2 残差网络（Residual Networks (ResNets)）
        • 2.3 残差网络为什么有用？（Why ResNets work?）
        • 2.4 网络中的网络以及 1×1 卷积（Network in Network and 1×1 convolutions）
        • 2.5 谷歌 Inception 网络简介（Inception network motivation）
        • 2.6 Inception 网络（Inception network）
        • 2.7 迁移学习（Transfer Learning）
        • 2.8 数据扩充（Data augmentation）
        • 2.9 计算机视觉现状（The state of computer vision）
        • Residual Networks
        • Keras tutorial - the Happy House
        • kt_utils.py
      • 第三周 目标检测（Object detection）
        • 3.1 目标定位（Object localization）
        • 3.2 特征点检测（Landmark detection）
        • 3.3 目标检测（Object detection）
        • 3.4 卷积的滑动窗口实现（Convolutional implementation of sliding windows）
        • 3.5 Bounding Box预测（Bounding box predictions）
        • 3.6 交并比（Intersection over union）
        • 3.7 非极大值抑制（Non-max suppression）
        • 3.8 Anchor Boxes
        • 3.9 YOLO 算法（Putting it together: YOLO algorithm）
        • 3.10 候选区域（选修）（Region proposals (Optional)）
        • Autonomous driving application - Car detection
        • yolo_utils.py
      • 第四周 特殊应用：人脸识别和神经风格转换（Special applications: Face recognition &Neural style transfer）
        • 4.1 什么是人脸识别？（What is face recognition?）
        • 4.2 One-Shot学习（One-shot learning）
        • 4.3 Siamese 网络（Siamese network）
        • 4.4 Triplet 损失（Triplet 损失）
        • 4.5 面部验证与二分类（Face verification and binary classification）
        • 4.6 什么是深度卷积网络？（What are deep ConvNets learning?）
        • 4.7 代价函数（Cost function）
        • 4.8 内容代价函数（Content cost function）
        • 4.9 风格代价函数（Style cost function）
        • 4.10 一维到三维推广（1D and 3D generalizations of models）
        • Art Generation with Neural Style Transfer
        • nst_utils.py
        • Face Recognition for the Happy House
        • fr_utils.py
        • inception_blocks.py
  • 第五门课 序列模型(Sequence Models)
    • 第五门课 序列模型(Sequence Models)
      • 第一周 循环序列模型（Recurrent Neural Networks）
        • 1.1 为什么选择序列模型？（Why Sequence Models?）
        • 1.2 数学符号（Notation）
        • 1.3 循环神经网络模型（Recurrent Neural Network Model）
        • 1.4 通过时间的反向传播（Backpropagation through time）
        • 1.5 不同类型的循环神经网络（Different types of RNNs）
        • 1.6 语言模型和序列生成（Language model and sequence generation）
        • 1.7 对新序列采样（Sampling novel sequences）
        • 1.8 循环神经网络的梯度消失（Vanishing gradients with RNNs）
        • 1.9 GRU单元（Gated Recurrent Unit（GRU））
        • 1.10 长短期记忆（LSTM（long short term memory）unit）
        • 1.11 双向循环神经网络（Bidirectional RNN）
        • 1.12 深层循环神经网络（Deep RNNs）
        • Building your Recurrent Neural Network
        • rnn_utils.py
        • Dinosaurus Island -- Character level language model final
        • utils.py
        • shakespeare_utils.py
        • Improvise a Jazz Solo with an LSTM Network
      • 第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）
        • 2.1 词汇表征（Word Representation）
        • 2.2 使用词嵌入（Using Word Embeddings）
        • 2.3 词嵌入的特性（Properties of Word Embeddings）
        • 2.4 嵌入矩阵（Embedding Matrix）
        • 2.5 学习词嵌入（Learning Word Embeddings）
        • 2.6 Word2Vec
        • 2.7 负采样（Negative Sampling）
        • 2.8 GloVe 词向量（GloVe Word Vectors）
        • 2.9 情感分类（Sentiment Classification）
        • 2.10 词嵌入除偏（Debiasing Word Embeddings）
        • Operations on word vectors
        • w2v_utils.py
        • Emojify
        • emo_utils.py
      • 第三周 序列模型和注意力机制（Sequence models & Attention mechanism）
        • 3.1 基础模型（Basic Models）
        • 3.2 选择最可能的句子（Picking the most likely sentence）
        • 3.3 集束搜索（Beam Search）
        • 3.4 改进集束搜索（Refinements to Beam Search）
        • 3.5 集束搜索的误差分析（Error analysis in beam search）
        • 3.6 Bleu 得分（选修）（Bleu Score (optional)）
        • 3.7 注意力模型直观理解（Attention Model Intuition）
        • 3.8注意力模型（Attention Model）
        • 3.9语音识别（Speech recognition）
        • 3.10触发字检测（Trigger Word Detection）
        • Neural machine translation with attention
        • nmt_utils.py
        • Trigger word detection
        • td_utils.py
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  • Keras tutorial - the Happy House
  • 1 - The Happy House
  • 2 - Building a model in Keras
  • 3 - Conclusion
  • 4 - Test with your own image (Optional)
  • 5 - Other useful functions in Keras (Optional)

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  1. 第四门课 卷积神经网络（Convolutional Neural Networks）
  2. 第四门课 卷积神经网络（Convolutional Neural Networks）
  3. 第二周 深度卷积网络：实例探究（Deep convolutional models: case studies）

Keras tutorial - the Happy House

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

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.

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. 
    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

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 = ...)

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 = 40, batch_size = 16)
### END CODE HERE ###
Epoch 1/40
600/600 [==============================] - 13s - loss: 1.5519 - acc: 0.6867    
Epoch 2/40
600/600 [==============================] - 13s - loss: 0.2373 - acc: 0.9100    
...  
Epoch 20/40
600/600 [==============================] - 14s - loss: 0.0266 - acc: 0.9933    
Epoch 21/40
...
Epoch 39/40
600/600 [==============================] - 15s - loss: 0.0661 - acc: 0.9833    
Epoch 40/40
600/600 [==============================] - 15s - loss: 0.0668 - acc: 0.9783    

<keras.callbacks.History at 0x7f3079fdb860>

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 [==============================] - 1s     

Loss = 0.127525748312
Test Accuracy = 0.96666667064

If your happyModel() function worked, you should have observed much better than random-guessing (50%) accuracy on the train and test sets.

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 a very good accuracy (let's say more than 80%), 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 a good 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/下载 (1).png'
### 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)
x = x/255
print(happyModel.predict(x))

"""
[[  2.88290081e-09]]
"""

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'))
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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.

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

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

Keras documentation