https://blog.csdn.net/JinbaoSite/article/details/77435558

from PIL import Image
from keras.utils import np_utils
from keras.preprocessing import image
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import History
history = History()

batch_size = 32
num_classes = 6
epochs = 100
img_width, img_height = 150, 150

def load_data():
    data = np.empty((510,150,150,3),dtype="float32")
    label = np.empty((510,))
    imgs = os.listdir(r"C:\Users\user\Desktop\birds\train")
    #num = len(imgs)
    num =510
    for i in range(num):
        if "egr" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 0
        elif "man" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 1
        elif "owl" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 2
        elif "puf" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 3
        elif "tou" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 4
        elif "wod" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/train/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 5
        else:
            break

    return data,label

train_data,train_labels = load_data()

y_train = keras.utils.to_categorical(train_labels, num_classes)
x_train = train_data.astype('float32')
x_train /= 255

model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',input_shape=train_data.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())

model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
              optimizer="adam",
              metrics=['accuracy'])
histroy=modela.fit(x_train, y_train,
              batch_size=batch_size,
              epochs=epochs,
              callbacks=[history],
               validation_data=(datatest, y_test),
              shuffle=True)

score = modela.evaluate(datatest, y_test, verbose=0)

def load_data1():

    data = np.empty((70,150,150,3),dtype="float32")
    label = np.empty((70,))
    imgs = os.listdir(r"C:\Users\user\Desktop\birds\test1")
    num = len(imgs)
    num = 70

    for i in range(num):
        if "egr" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")#test1要再改
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 0
        elif "man" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 1
        elif "owl" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 2
        elif "puf" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 3
        elif "tou" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 4
        elif "wod" in imgs[i]:
            img = Image.open("C:/Users/user/Desktop/birds/test1/" + imgs[i])
            arr = np.asarray(img, dtype="float32")
            arr.resize((150,150,3))
            data[i, :, :, :] = arr
            label[i] = 5
        else:
            break

    return data,label

datatest,labeltest = load_data1()
datatest=datatest.astype("float32")/255

y_test=np_utils.to_categorical(labeltest,num_classes=6)
predict=model.predict_classes(datatest)
import pandas as d

pd.crosstab(labeltest.reshape(-1),predict,rownames=["label"],colnames=["predict"])
score = model.evaluate(datatest, y_test, verbose=0)
history.loss_plot('epoch')
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = {'batch': [], 'epoch': []}
        self.accuracy = {'batch': [], 'epoch': []}
        self.val_loss = {'batch': [], 'epoch': []}
        self.val_acc = {'batch': [], 'epoch': []}

    def on_batch_end(self, batch, logs={}):
        self.losses['batch'].append(logs.get('loss'))
        self.accuracy['batch'].append(logs.get('acc'))
        self.val_loss['batch'].append(logs.get('val_loss'))
        self.val_acc['batch'].append(logs.get('val_acc'))

    def on_epoch_end(self, batch, logs={}):
        self.losses['epoch'].append(logs.get('loss'))
        self.accuracy['epoch'].append(logs.get('acc'))
        self.val_loss['epoch'].append(logs.get('val_loss'))
        self.val_acc['epoch'].append(logs.get('val_acc'))

    def loss_plot(self, loss_type):
        iters = range(len(self.losses[loss_type]))
        #创建一个图
        plt.figure()
        # acc
        plt.plot(iters, self.accuracy[loss_type], 'r', label='train
acc')#plt.plot(x,y)，这个将数据画成曲线
        # loss
        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
        if loss_type == 'epoch':
            # val_acc
            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
            # val_loss
            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
        plt.grid(True)#设置网格形式
        plt.xlabel(loss_type)
        plt.ylabel('acc-loss')#给x，y轴加注释
        plt.legend(loc="upper right")#设置图例显示位置
        plt.show()

history = LossHistory()