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net_training.py

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+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')