from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Conv2D, Flatten, Dense, AvgPool2D

from tensorflow.keras.optimizers import Adam

 

 

def load_train(path):

    datagen = ImageDataGenerator(rescale=1./255.,horizontal_flip=True,

                                       vertical_flip=True)

    train_datagen_flow  = datagen.flow_from_directory(

        path,

        target_size=(150, 150),

        batch_size=16,

        calss_mode='sparse',

        seed=12345)

    return train_datagen_flow

 

 

# Создание модели

def create_model(input_shape):

    model = Sequential()

    model.add(Conv2D(filters=6, kernel_size=(5, 5), padding='same', activation='relu', input_shape=input_shape))

    model.add(AvgPool2D(pool_size=(2, 2)))

    model.add(Conv2D(filters=14, kernel_size=(5, 5),activation='relu'))

    model.add(AvgPool2D(pool_size=(2, 2)))

    model.add(Flatten())

    model.add(Dense(80, activation='relu'))

    model.add(Dense(12, activation='softmax')),

    optimizer = Adam(lr=0.005),

    model.compile(optimizer=optimizer,

                  loss='sparse_categorical_crossentropy',

                  metrics=['acc'])

    return model

 

 

def train_model(model, train_data, test_data, batch_size=None, epochs=10, steps_per_epoch=None, validation_steps=None):

    model.fit(train_data,

          validation_data=test_data,

          batch_size = batch_size,

          steps_per_epoch=steps_per_epoch,

          validation_steps=validation_steps,

          epochs=epochs,verbose=2,

            shuffle=True )

    return model