import os
import random

import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet import preprocess_input, decode_predictions
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator

podstawa_modelu = MobileNetV2(weights="imagenet", include_top=False, pooling='avg')
x = podstawa_modelu.output
preds = Dense(7, activation='softmax')(x)

model = Model(inputs=podstawa_modelu.input, outputs=preds)

for layer in model.layers[:50]:
    layer.trainable = False
for layer in model.layers[50:]:
    layer.trainable = True

train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

train_generator = train_datagen.flow_from_directory('./dataset', target_size=(224, 224), color_mode='rgb',
                                                    batch_size=32, class_mode='categorical', shuffle=True)

model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
step_size_train = train_generator.n // train_generator.batch_size
model.fit_generator(generator=train_generator, steps_per_epoch=step_size_train, epochs=30)

model.save('neural_model.h5')