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目录

• 1 使用复杂图像
• 2 获取数据
• 3 使用ImageGenerator标记和准备数据¶
• 4 探索数据
• 5 定义模型
• 6 编译模型
• 7 ImageGenerator生成数据
• 8 训练
• 9 测试模型
• 10 可视化中间过程

1 使用复杂图像：马或人

在之前的Fashion MNIST训练图像分类器中。图像时28×28，并且图像居中。本节我们将提升一个新的水平，训练识别图像中的特征，其中主体可以在图像中的任何位置。

2 获取数据

我们将构建马匹和人类的分类来实现上述目的，该分类器将告诉图像是否包含马或人。

import osimport zipfilelocal_zip = "./horse-or-human.zip"zip_ref = zipfile.ZipFile(local_zip,"r")zip_ref.extractall("./horse-or-human")zip_ref.close()

.zip的内容被提取到基本目录horse-or-human中

3 使用ImageGenerator标记和准备数据

在这个样本中我们要注意到一件事是：我们没有明确地将图像标记为马或人。

下面我们将用ImageGenerator来从子目录中读取图像，并从该子目录的名称自动标记它们。

# directory with out trainging horse picturestrain_horse_dir = os.path.join("./horse-or-human/horses")# dirctory with out training human picturestrain_human_dir = os.path.join("./horse-or-human/humans")

现在，让我们看一下马和人训练目录中的文件名是怎么样的

train_horse_names = os.listdir(train_horse_dir)print(train_horse_names[:10])train_human_names = os.listdir(train_human_dir)print(train_human_names[:10])

['horse01-0.png', 'horse01-1.png', 'horse01-2.png', 'horse01-3.png', 'horse01-4.png', 'horse01-5.png', 'horse01-6.png', 'horse01-7.png', 'horse01-8.png', 'horse01-9.png']['human01-00.png', 'human01-01.png', 'human01-02.png', 'human01-03.png', 'human01-04.png', 'human01-05.png', 'human01-06.png', 'human01-07.png', 'human01-08.png', 'human01-09.png']

让我们找出目录中马和人类图像的总数：

print("the total training horse images:",len(os.listdir(train_horse_dir)))print("the total training human images:",len(os.listdir(train_human_dir)))

the total training horse images: 500the total training human images: 527

4 探索数据

现在我们看看几张照片，以便更好地了解它们的外观。

import matplotlib.pyplot as plt%matplotlib inlineimport matplotlib.image as mpimgnrows = 4ncols = 4pic_index = 0

现在展示一批8张马和8张人的图片

# Set up matplotlib fig, and size it to fit 4x4 picsfig = plt.gcf()fig.set_size_inches(ncols * 4, nrows * 4) pic_index += 8next_horse_pix = [os.path.join(train_horse_dir, fname)                 for fname in train_horse_names[pic_index-8:pic_index]]next_human_pix = [os.path.join(train_human_dir, fname)                 for fname in train_human_names[pic_index-8:pic_index]] for i, img_path in enumerate(next_horse_pix+next_human_pix):  # Set up subplot; subplot indices start at 1    sp = plt.subplot(nrows, ncols, i + 1 )    sp.axis('Off') # Don't show axes (or gridlines)        img = mpimg.imread(img_path)    plt.imshow(img)

5 定义模型

第1步导入TensorFlow

import tensorflow as tf

这是一个二分类问题

model = tf.keras.models.Sequential([    tf.keras.layers.Conv2D(16,(3,3),activation="relu",input_shape=(300,300,3)),    tf.keras.layers.MaxPooling2D(2,2),        tf.keras.layers.Conv2D(32,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),        tf.keras.layers.Conv2D(64,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),        tf.keras.layers.Conv2D(64,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),        tf.keras.layers.Conv2D(64,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),        tf.keras.layers.Flatten(),    tf.keras.layers.Dense(512,activation="relu"),    tf.keras.layers.Dense(1,activation="sigmoid")])

WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.Instructions for updating:Colocations handled automatically by placer.

model.summary打印模型信息

model.summary()

_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================conv2d (Conv2D)              (None, 298, 298, 16)      448       _________________________________________________________________max_pooling2d (MaxPooling2D) (None, 149, 149, 16)      0         _________________________________________________________________conv2d_1 (Conv2D)            (None, 147, 147, 32)      4640      _________________________________________________________________max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32)        0         _________________________________________________________________conv2d_2 (Conv2D)            (None, 71, 71, 64)        18496     _________________________________________________________________max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64)        0         _________________________________________________________________conv2d_3 (Conv2D)            (None, 33, 33, 64)        36928     _________________________________________________________________max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64)        0         _________________________________________________________________conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     _________________________________________________________________max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         _________________________________________________________________flatten (Flatten)            (None, 3136)              0         _________________________________________________________________dense (Dense)                (None, 512)               1606144   _________________________________________________________________dense_1 (Dense)              (None, 1)                 513       =================================================================Total params: 1,704,097Trainable params: 1,704,097Non-trainable params: 0_________________________________________________________________

6 编译模型

from tensorflow.keras.optimizers import RMSpropmodel.compile(loss="binary_crossentropy",              optimizer=RMSprop(lr=0.001),              metrics=["acc"])

7 ImageGenerator生成数据

from tensorflow.keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(    "./horse-or-human/",    target_size=(300,300),    batch_size=128,    class_mode="binary")

Found 1027 images belonging to 2 classes.

8 训练

history = model.fit_generator(    train_generator,    steps_per_epoch=8,    epochs=15,    verbose=1)

WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.Instructions for updating:Use tf.cast instead.Epoch 1/159/9 [==============================] - 85s 9s/step - loss: 0.9717 - acc: 0.5511Epoch 2/159/9 [==============================] - 78s 9s/step - loss: 0.5993 - acc: 0.7527Epoch 3/159/9 [==============================] - 81s 9s/step - loss: 0.7818 - acc: 0.7858Epoch 4/159/9 [==============================] - 82s 9s/step - loss: 0.4991 - acc: 0.8043Epoch 5/159/9 [==============================] - 88s 10s/step - loss: 0.2824 - acc: 0.8939Epoch 6/159/9 [==============================] - 74s 8s/step - loss: 0.1238 - acc: 0.9659Epoch 7/159/9 [==============================] - 83s 9s/step - loss: 0.0556 - acc: 0.9747Epoch 8/159/9 [==============================] - 74s 8s/step - loss: 0.7515 - acc: 0.8822Epoch 9/159/9 [==============================] - 73s 8s/step - loss: 0.0387 - acc: 0.9903Epoch 10/159/9 [==============================] - 74s 8s/step - loss: 0.0553 - acc: 0.9718Epoch 11/159/9 [==============================] - 74s 8s/step - loss: 0.0151 - acc: 0.9961Epoch 12/159/9 [==============================] - 72s 8s/step - loss: 0.0043 - acc: 1.0000Epoch 13/159/9 [==============================] - 75s 8s/step - loss: 0.4870 - acc: 0.8812Epoch 14/159/9 [==============================] - 72s 8s/step - loss: 0.0156 - acc: 0.9961Epoch 15/159/9 [==============================] - 75s 8s/step - loss: 0.0468 - acc: 0.9805

9 测试模型

现在让我们使用该模型实际运行预测。此代码允许你从文件系统中选择一个或多个文件，然后上传它们，并通过模型运行它们，指示对象是马或人

import cv2import numpy as nphuman_img = cv2.imread("human.jpg")plt.imshow(human_img)classes = model.predict(human_img.reshape(1,300,300,3))print(classes[0])if classes[0] > 0.5:    print("this is a human")else:    print("this is a horse")

[1.]this is a human

import cv2import numpy as nphorse_img = cv2.imread("horse.jpg")plt.imshow(horse_img)classes = model.predict(horse_img.reshape(1,300,300,3))print(classes[0])if classes[0] > 0.5:    print("this is a human")else:    print("this is a horse")

[1.]this is a human

可以看到分辨的话还是不准确的

10 可视化中间过程

从训练集中随机选择一个图像，然后生成一个图像，其中每一行是图层的输出，并且该行输出特征图中的特定 滤镜。

import numpy as npimport randomfrom tensorflow.keras.preprocessing.image import img_to_array, load_img # Let's define a new Model that will take an image as input, and will output# intermediate representations for all layers in the previous model after# the first.successive_outputs = [layer.output for layer in model.layers[1:]]#visualization_model = Model(img_input, successive_outputs)visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)# Let's prepare a random input image from the training set.horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names]human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names]img_path = random.choice(horse_img_files + human_img_files) img = load_img(img_path, target_size=(300, 300))  # this is a PIL imagex = img_to_array(img)  # Numpy array with shape (150, 150, 3)x = x.reshape((1,) + x.shape)  # Numpy array with shape (1, 150, 150, 3) # Rescale by 1/255x /= 255 # Let's run our image through our network, thus obtaining all# intermediate representations for this image.successive_feature_maps = visualization_model.predict(x) # These are the names of the layers, so can have them as part of our plotlayer_names = [layer.name for layer in model.layers] # Now let's display our representationsfor layer_name, feature_map in zip(layer_names, successive_feature_maps):    if len(feature_map.shape) == 4:        # Just do this for the conv / maxpool layers, not the fully-connected layers        n_features = feature_map.shape[-1]  # number of features in feature map        # The feature map has shape (1, size, size, n_features)        size = feature_map.shape[1]        # We will tile our images in this matrix        display_grid = np.zeros((size, size * n_features))        for i in range(n_features):            # Postprocess the feature to make it visually palatable            x = feature_map[0, :, :, i]            x -= x.mean()            x /= x.std()            x *= 64            x += 128            x = np.clip(x, 0, 255).astype('uint8')            # We'll tile each filter into this big horizontal grid            display_grid[:, i * size : (i + 1) * size] = x        # Display the grid        scale = 20. / n_features        plt.figure(figsize=(scale * n_features, scale))        plt.title(layer_name)        plt.grid(False)        plt.imshow(display_grid, aspect='auto', cmap='viridis')

D:\software\Anaconda\anaconda\lib\site-packages\ipykernel_launcher.py:43: RuntimeWarning: invalid value encountered in true_divide

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