keras实战-卷积神经网络图片分类-猫狗数据

from keras.datasets import mnist
from keras.utils import np_utils #convert int labels to one-hot vector
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten
from keras.models import Sequential
import os
import numpy as np
from keras import optimizers
from keras.utils.np_utils import to_categorical
from scipy.misc import imread,imresize
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator

imgs = []
labels = []
img_shape = (150,150)
#image generator
files = os.listdir('data/test')
#read 1000 files for the generator
for img_file in files[:1000]:
    img = imread('data/test/'+img_file).astype("float32")
    img = imresize(img,img_shape)
    imgs.append(img)

imgs = np.array(imgs)
train_gen = ImageDataGenerator(
    # rescale=1./255,
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True
)

val_gen = ImageDataGenerator(
    # rescale=1./255,
    featurewise_center=True,
    featurewise_std_normalization=True
)

train_gen.fit(imgs)
val_gen.fit(imgs)

#4500 training images
train_iter = train_gen.flow_from_directory('data/train',class_mode='binary',target_size=img_shape,batch_size=16)

#501 validation images
val_iter = val_gen.flow_from_directory('data/val',class_mode='binary',target_size=img_shape,batch_size=16)


#define the navie model using sequential model
model = Sequential()
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(128, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(128, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.summary()

#define the optimzers
model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4),
        metrics=['acc'])


history = model.fit_generator(generator=train_iter,
                              steps_per_epoch=282,
                              epochs=100,
                              validation_data=val_iter,
                              validation_steps=32
                              )

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1,101)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.legend()
plt.show()