0.1331 val_acc code

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Conv2D, Flatten, Dense, AvgPool2D
from tensorflow.keras.models import Sequential


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

    train_datagen_flow = train_datagen.flow_from_directory(
        path,
        target_size=(150, 150),
        batch_size=16,
        class_mode='sparse',
        subset='training',
        seed=12345)
    return train_datagen_flow


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(Flatten())
    model.add(Dense(units=20, activation='relu'))
    model.add(Dense(units=20, activation='softmax'))
    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])

    return model


def train_model(model, train_data, test_data, batch_size=None, epochs=5,
                steps_per_epoch=None, validation_steps=None):
    model.fit(train_data,
              validation_data=test_data,
              batch_size=batch_size, epochs=epochs,
              steps_per_epoch=steps_per_epoch,
              validation_steps=validation_steps,
              verbose=2, shuffle=True)

    return model