овощи

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, Dense, AvgPool2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.optimizers import Adam
optimizer = Adam()


def load_train(path):
    datagen = ImageDataGenerator(validation_split=0.25,
                             rescale=1./255)
    train_datagen = ImageDataGenerator(
    validation_split=0.25,
    rescale=1./255,
    horizontal_flip=True,
    vertical_flip= True)

    validation_datagen = ImageDataGenerator(
    validation_split=0.25,
    rescale=1./255)

    train_datagen_flow = datagen.flow_from_directory(path,
    '/datasets/fruits_small/',
    target_size=(150, 150),
    batch_size=16,
    class_mode='sparse',
    subset='training',
    seed=12345)

    val_datagen_flow = datagen.flow_from_directory(path,
    '/datasets/fruits_small/',
    target_size=(150, 150),
    batch_size=16,
    class_mode='sparse',
    subset='validation',
    seed=12345)

    features_train, target_train = next(train_datagen_flow)
    features_test, target_test = next(val_datagen_flow)

    return features_train, target_train


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=16, kernel_size=(5, 5), padding='valid',strides = 1,
                 activation='relu'))
    model.add(AvgPool2D(pool_size =(2,2)))
    model.add(Flatten())
    model.add(Dense(units=120, activation='relu'))
    model.add(Dense(units=84, activation='relu'))
    model.add(Dense(units=10, activation='softmax'))
    model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy',
                  metrics=['acc'])

    return model


def train_model(model, train_data, test_data, batch_size=16, epochs=5,
               steps_per_epoch=None, validation_steps=None):

    features_train, target_train = train_data
    features_test, target_test = test_data
    model.fit(features_train, target_train,
              validation_data=(features_test, target_test),
              batch_size=batch_size, epochs=epochs,
              steps_per_epoch=steps_per_epoch,
              validation_steps=validation_steps,
              verbose=2, shuffle=True)

    return model