机器学习示例一（代码绝对能跑！）

源码来源：https://blog.youkuaiyun.com/liangyihuai/article/details/90544066

题目：区别猫和狗的图片，猫、狗的训练图片各
1000张，测试图片各500张，验证图片各500张。

可直接全部复制粘贴到colab上运行

# -*- coding: utf-8 -*-
"""import file from google driver.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1k_1MmC8jY5HYoaPcq7AQa5t7G84Wu4qL
"""

import tensorflow as tf
tf.test.gpu_device_name()

import os, shutil
from keras import Sequential, losses, metrics
from keras.layers import Flatten, Conv2D, MaxPool2D, Dense, Dropout
from keras import optimizers

from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
import matplotlib.pyplot as plt

! git clone https://gitlab.com/liangyihuai/cats_and_dogs_small.git

! ls

! ls cats_and_dogs_small/

base_dir = 'cats_and_dogs_small'


train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')

train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
test_cats_dir = os.path.join(test_dir, 'cats')
test_dogs_dir = os.path.join(test_dir, 'dogs')

print('total training cat images: ', len(os.listdir(train_cats_dir)))
print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))

datagen = ImageDataGenerator(
                            rotation_range=40,
                            width_shift_range=0.2,
                            height_shift_range=0.2,
                            shear_range=0.2,
                            zoom_range=0.2,
                            horizontal_flip=True,
                            fill_mode='nearest')

fnames = [os.path.join(train_cats_dir, fname) for 
          fname in os.listdir(train_cats_dir)]
img_path = fnames[1]
img = image.load_img(img_path, target_size=(150, 150))
x = image.img_to_array(img)
x = x.reshape((1,)+x.shape)

i = 0
for batch in datagen.flow(x, batch_size=1):
    plt.figure(i)
    imgplot = plt.imshow(image.array_to_img(batch[0]))
    i += 1
    if i % 4 == 0:
        break

plt.show()

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss=losses.binary_crossentropy, 
              optimizer=optimizers.RMSprop(lr=1e-4),
             metrics=[metrics.binary_accuracy])

train_datagen = ImageDataGenerator(
                                rescale=1./255,
                                rotation_range=40,
                                width_shift_range=0.2,
                                height_shift_range=0.2,
                                shear_range=0.2,
                                zoom_range=0.2,
                                horizontal_flip=True,)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    train_dir, 
    target_size=(150, 150), 
    batch_size=32, 
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='binary')

history = model.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=100,
    validation_data=validation_generator,
    validation_steps=50)

model.save('cats_and_dogs_small_2.h5')

acc = history.history['binary_accuracy']
val_acc = history.history['val_binary_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

代码运行结果：
https://colab.research.google.com/drive/1k_1MmC8jY5HYoaPcq7AQa5t7G84Wu4qL#scrollTo=dUyqA2dvvA_F