资料下载
链接：https://pan.baidu.com/s/1xANUtgIWgt7gcul6dnInhA
提取码：oajj，请在开始之前下载好所需资料
1 - Packages
首先，我们要导入一些库
import math import numpy as np import h5py import matplotlib.pyplot as plt from matplotlib.pyplot import imread import scipy from PIL import Image import pandas as pd import tensorflow as tf import tensorflow.keras.layers as tfl from tensorflow.python.framework import ops from cnn_utils import * from test_utils import summary, comparator %matplotlib inline np.random.seed(1)
数据集我们使用的是 Happy House dataset
加载一下数据集吧！
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_happy_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)
结果发现我们使用的是64✖64，3通道的图片
我们随机的看张图片
index = 120 plt.imshow(X_train_orig[index]) #display sample training image plt.show()
happyModel
- ZeroPadding2D: 填充三层 ，输入应该为64 x 64 x 3
- Conv2D: 使用 32 个7x7 filters, stride 1
- BatchNormalization: for axis 3
- ReLU
- MaxPool2D: Using default parameters
- Flatten the previous output.
Fully-connected (Dense) layer: Apply a fully connected layer with 1 neuron and a sigmoid activation.
def happyModel(): """ 实现二进制分类模型的正向传播： ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE 注意，为了简化和分级，您将对所有值进行硬编码 例如步幅和内核（滤波器）大小。 通常，函数应将这些值作为函数参数。 Arguments: None Returns: model -- TF Keras model (包含整个培训过程信息的对象) """ model = tf.keras.Sequential([ tf.keras.Input(shape=(64 , 64 ,3)), ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3 tfl.ZeroPadding2D(padding=3), # As import tensorflow.keras.layers as tfl ## Conv2D with 32 7x7 filters and stride of 1 tfl.Conv2D(filters=32,kernel_size=7,strides=1), ## BatchNormalization for axis 3 tfl.BatchNormalization(axis=3, momentum=0.99, epsilon=0.001), ## ReLU tfl.ReLU(), ## Max Pooling 2D with default parameters tfl.MaxPool2D(), ## Flatten layer tfl.Flatten(), ## Dense layer with 1 unit for output & 'sigmoid' activation tfl.Dense(1,activation='sigmoid') ]) return model
我们来测试一下：
happy_model = happyModel() # Print a summary for each layer for layer in summary(happy_model): print(layer)
['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))] ['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform'] ['BatchNormalization', (None, 64, 64, 32), 128] ['ReLU', (None, 64, 64, 32), 0] ['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid'] ['Flatten', (None, 32768), 0] ['Dense', (None, 1), 32769, 'sigmoid']
现在您的模型已经创建，您可以编译它，以便使用优化器进行训练，而不必选择。当字符串精度指定为度量时，所使用的精度类型将根据所使用的损失函数自动转换。这是TensorFlow内置的众多优化之一，可以让您的生活更轻松！
好了，让我们去编译它吧！
happy_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
是时候用.summary（）方法检查模型的参数了。这将显示您拥有的图层类型、输出的形状以及每个图层中的参数数量。
happy_model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d (ZeroPadding (None, 70, 70, 3) 0
2D)
conv2d (Conv2D) (None, 64, 64, 32) 4736
batch_normalization (BatchN (None, 64, 64, 32) 128
ormalization)
re_lu (ReLU) (None, 64, 64, 32) 0
max_pooling2d (MaxPooling2D (None, 32, 32, 32) 0
)
flatten (Flatten) (None, 32768) 0
dense (Dense) (None, 1) 32769
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64
_________________________________________________________________
Train and Evaluate the Model
在创建了模型，使用您选择的优化器和损失函数对其进行编译，并对其内容进行了完整性检查之后，现在就可以开始构建了！
只需调用.fit（）进行训练。就是这样！无需进行小批量、节省或复杂的反向传播计算。这一切都是为您完成的，因为您使用的是已经指定批次的TensorFlow数据集。如果您愿意，您可以选择指定纪元编号或小批量大小（例如，在未批处理数据集的情况下）。
happy_model.evaluate(X_test, Y_test)
Out[28]:
[0.14378029108047485, 0.9399999976158142]
很简单，对吧？但是，如果您需要构建一个具有共享层、分支或多个输入和输出的模型呢？这正是Sequential凭借其简单而有限的功能无法帮助您的地方。
下一步：进入Functional API，这是一个稍微复杂、高度灵活的朋友。
欢迎来到作业的下半部分，您将使用Keras灵活的Functional API构建一个ConvNet，它可以区分6个手语数字。
Functional API可以处理具有非线性拓扑、共享层以及具有多个输入或输出的层的模型。想象一下，当Sequential API要求模型以线性方式在其层中移动时，Functional API允许更大的灵活性。在Sequential是一条直线的情况下，Functional模型是一个图，其中层的节点可以以多种方式连接。
在下面的视觉示例中，顺序模型显示了一个可能的运动方向，与跳过连接形成对比，这只是构建功能模型的多种方式之一。您可能已经猜到，跳过连接会跳过网络中的某一层，并将输出馈送到网络中的稍后一层。别担心，你很快就会花更多时间在跳过连接上！
我们加载一下数据集
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_signs_dataset()
随机看一个数据
index = 4 plt.imshow(X_train_orig[index]) print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
y = 2
在之前的学习中，
您为该数据集构建了一个完全连接的网络。但由于这是一个图像数据集，因此将ConvNet应用于它更为自然。
首先，让我们检查数据的形状。
X_train = X_train_orig/255. X_test = X_test_orig/255. 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)
Forward Propagation
- Conv2D:8个4✖4的 filters, stride 1, 填充 is "SAME"
- ReLU
- MaxPool2D: 使用一个8✖8 filter size and 一个（8，8）步长, padding is "SAME"
- Conv2D: Use 16 2 by 2 filters, stride 1, padding is "SAME"
- ReLU
- MaxPool2D: Use a 4 by 4 filter size and a 4 by 4 stride, padding is "SAME"
- Flatten the previous output.
- Fully-connected (Dense) layer:应用具有6个神经元的完全连接层和softmax激活。
下面我们建立模型
def convolutional_model(input_shape): """ Implements the forward propagation for the model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE 注意，为了简化和分级，您将硬编码一些值 例如步幅和内核（滤波器）大小。 通常，函数应将这些值作为函数参数。 Arguments: input_img -- input dataset, of shape (input_shape) Returns: model -- TF Keras model (包含整个培训过程信息的对象) """ input_img = tf.keras.Input(shape=input_shape) ## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME' Z1 = tfl.Conv2D(filters= 8. , kernel_size=4 , padding='same',strides=1)(input_img) ## RELU A1 = tfl.ReLU()(Z1) ## MAXPOOL: window 8x8, stride 8, padding 'SAME' P1 = tfl.MaxPool2D(pool_size=8, strides=8, padding='SAME')(A1) ## CONV2D: 16 filters 2x2, stride 1, padding 'SAME' Z2 = tfl.Conv2D(filters= 16. , kernel_size=2 , padding='same',strides=1)(P1) ## RELU A2 = tfl.ReLU()(Z2) ## MAXPOOL: window 4x4, stride 4, padding 'SAME' P2 = tfl.MaxPool2D(pool_size=4, strides=4, padding='SAME')(A2) ## FLATTEN F = tfl.Flatten()(P2) ## 全连接层 ##输出层6个神经元。提示：其中一个参数应该是“activation='softmax'” outputs = tfl.Dense(units= 6 , activation='softmax')(F) model = tf.keras.Model(inputs=input_img, outputs=outputs) return model
编译并检查模型的参数
conv_model = convolutional_model((64, 64, 3)) conv_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) conv_model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 64, 64, 3)] 0
conv2d_1 (Conv2D) (None, 64, 64, 8) 392
re_lu_1 (ReLU) (None, 64, 64, 8) 0
max_pooling2d_1 (MaxPooling (None, 8, 8, 8) 0
2D)
conv2d_2 (Conv2D) (None, 8, 8, 16) 528
re_lu_2 (ReLU) (None, 8, 8, 16) 0
max_pooling2d_2 (MaxPooling (None, 2, 2, 16) 0
2D)
flatten_1 (Flatten) (None, 64) 0
dense_1 (Dense) (None, 6) 390
=================================================================
Total params: 1,310
Trainable params: 1,310
Non-trainable params: 0
_________________________________________________________________
Train the Model
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)).batch(64) test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)).batch(64) history = conv_model.fit(train_dataset, epochs=100, validation_data=test_dataset)
Epoch 59/100 17/17 [==============================] - 1s 63ms/step - loss: 0.5240 - accuracy: 0.8296 - val_loss: 0.6141 - val_accuracy: 0.8000 Epoch 60/100 17/17 [==============================] - 1s 45ms/step - loss: 0.5175 - accuracy: 0.8333 - val_loss: 0.6084 - val_accuracy: 0.8083 Epoch 61/100 17/17 [==============================] - 1s 53ms/step - loss: 0.5093 - accuracy: 0.8352 - val_loss: 0.6047 - val_accuracy: 0.8250 Epoch 62/100 17/17 [==============================] - 1s 50ms/step - loss: 0.5044 - accuracy: 0.8370 - val_loss: 0.5991 - val_accuracy: 0.8083 Epoch 63/100 17/17 [==============================] - 1s 53ms/step - loss: 0.4981 - accuracy: 0.8333 - val_loss: 0.5955 - val_accuracy: 0.8167 Epoch 64/100 17/17 [==============================] - 1s 57ms/step - loss: 0.4902 - accuracy: 0.8380 - val_loss: 0.5926 - val_accuracy: 0.8250 Epoch 65/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4853 - accuracy: 0.8444 - val_loss: 0.5882 - val_accuracy: 0.8083 Epoch 66/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4794 - accuracy: 0.8472 - val_loss: 0.5839 - val_accuracy: 0.8083 Epoch 67/100 17/17 [==============================] - 1s 44ms/step - loss: 0.4724 - accuracy: 0.8519 - val_loss: 0.5809 - val_accuracy: 0.8167 Epoch 68/100 17/17 [==============================] - 1s 54ms/step - loss: 0.4680 - accuracy: 0.8528 - val_loss: 0.5760 - val_accuracy: 0.8083 Epoch 69/100 17/17 [==============================] - 1s 46ms/step - loss: 0.4623 - accuracy: 0.8546 - val_loss: 0.5719 - val_accuracy: 0.8250 Epoch 70/100 17/17 [==============================] - 1s 49ms/step - loss: 0.4559 - accuracy: 0.8574 - val_loss: 0.5692 - val_accuracy: 0.8083 Epoch 71/100 17/17 [==============================] - 1s 57ms/step - loss: 0.4518 - accuracy: 0.8593 - val_loss: 0.5650 - val_accuracy: 0.8083 Epoch 72/100 17/17 [==============================] - 1s 60ms/step - loss: 0.4452 - accuracy: 0.8620 - val_loss: 0.5624 - val_accuracy: 0.8250 Epoch 73/100 17/17 [==============================] - 1s 52ms/step - loss: 0.4415 - accuracy: 0.8639 - val_loss: 0.5590 - val_accuracy: 0.8167 Epoch 74/100 17/17 [==============================] - 1s 55ms/step - loss: 0.4361 - accuracy: 0.8648 - val_loss: 0.5554 - val_accuracy: 0.8250 Epoch 75/100 17/17 [==============================] - 1s 58ms/step - loss: 0.4298 - accuracy: 0.8704 - val_loss: 0.5528 - val_accuracy: 0.8333 Epoch 76/100 17/17 [==============================] - 1s 62ms/step - loss: 0.4262 - accuracy: 0.8685 - val_loss: 0.5490 - val_accuracy: 0.8250 Epoch 77/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4215 - accuracy: 0.8713 - val_loss: 0.5460 - val_accuracy: 0.8250 Epoch 78/100 17/17 [==============================] - 1s 46ms/step - loss: 0.4151 - accuracy: 0.8787 - val_loss: 0.5436 - val_accuracy: 0.8250 Epoch 79/100 17/17 [==============================] - 1s 43ms/step - loss: 0.4113 - accuracy: 0.8787 - val_loss: 0.5407 - val_accuracy: 0.8167 Epoch 80/100 17/17 [==============================] - 1s 43ms/step - loss: 0.4062 - accuracy: 0.8806 - val_loss: 0.5384 - val_accuracy: 0.8167 Epoch 81/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4020 - accuracy: 0.8806 - val_loss: 0.5348 - val_accuracy: 0.8167 Epoch 82/100 17/17 [==============================] - 1s 50ms/step - loss: 0.3962 - accuracy: 0.8824 - val_loss: 0.5323 - val_accuracy: 0.8167 Epoch 83/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3927 - accuracy: 0.8824 - val_loss: 0.5297 - val_accuracy: 0.8250 Epoch 84/100 17/17 [==============================] - 1s 51ms/step - loss: 0.3881 - accuracy: 0.8843 - val_loss: 0.5272 - val_accuracy: 0.8250 Epoch 85/100 17/17 [==============================] - 1s 46ms/step - loss: 0.3832 - accuracy: 0.8870 - val_loss: 0.5249 - val_accuracy: 0.8250 Epoch 86/100 17/17 [==============================] - 1s 53ms/step - loss: 0.3796 - accuracy: 0.8898 - val_loss: 0.5215 - val_accuracy: 0.8250 Epoch 87/100 17/17 [==============================] - 1s 56ms/step - loss: 0.3743 - accuracy: 0.8889 - val_loss: 0.5196 - val_accuracy: 0.8250 Epoch 88/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3710 - accuracy: 0.8907 - val_loss: 0.5164 - val_accuracy: 0.8250 Epoch 89/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3660 - accuracy: 0.8917 - val_loss: 0.5139 - val_accuracy: 0.8333 Epoch 90/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3626 - accuracy: 0.8917 - val_loss: 0.5106 - val_accuracy: 0.8333 Epoch 91/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3579 - accuracy: 0.8944 - val_loss: 0.5090 - val_accuracy: 0.8500 Epoch 92/100 17/17 [==============================] - 1s 49ms/step - loss: 0.3547 - accuracy: 0.8935 - val_loss: 0.5060 - val_accuracy: 0.8417 Epoch 93/100 17/17 [==============================] - 1s 44ms/step - loss: 0.3501 - accuracy: 0.8944 - val_loss: 0.5038 - val_accuracy: 0.8500 Epoch 94/100 17/17 [==============================] - 1s 47ms/step - loss: 0.3468 - accuracy: 0.8954 - val_loss: 0.5014 - val_accuracy: 0.8417 Epoch 95/100 17/17 [==============================] - 1s 43ms/step - loss: 0.3424 - accuracy: 0.8954 - val_loss: 0.4996 - val_accuracy: 0.8500 Epoch 96/100 17/17 [==============================] - 1s 64ms/step - loss: 0.3395 - accuracy: 0.8963 - val_loss: 0.4970 - val_accuracy: 0.8417 Epoch 97/100 17/17 [==============================] - 1s 47ms/step - loss: 0.3351 - accuracy: 0.9000 - val_loss: 0.4950 - val_accuracy: 0.8417 Epoch 98/100 17/17 [==============================] - 1s 54ms/step - loss: 0.3323 - accuracy: 0.8981 - val_loss: 0.4933 - val_accuracy: 0.8333 Epoch 99/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3280 - accuracy: 0.9000 - val_loss: 0.4916 - val_accuracy: 0.8417 Epoch 100/100 17/17 [==============================] - 1s 57ms/step - loss: 0.3251 - accuracy: 0.9028 - val_loss: 0.4894 - val_accuracy: 0.8333
history对象是.fit（）操作的输出，并提供内存中所有损失和度量值的记录。它存储为字典，您可以在history中检索。history：
history.history
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0.8416666388511658,
0.8416666388511658,
0.8333333134651184,
0.8416666388511658,
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现在，使用history.history可视化时间损失：
df_loss_acc = pd.DataFrame(history.history) df_loss= df_loss_acc[['loss','val_loss']] df_loss.rename(columns={'loss':'train','val_loss':'validation'},inplace=True) df_acc= df_loss_acc[['accuracy','val_accuracy']] df_acc.rename(columns={'accuracy':'train','val_accuracy':'validation'},inplace=True) df_loss.plot(title='Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss') df_acc.plot(title='Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy') plt.show()
