Init

.gitignore

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+my_models

src/fit_model.py

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+import tensorflow as tf
+from tensorflow import keras
+
+# dataset utilizzato https://keras.io/api/datasets/fashion_mnist/#load_data-function
+
+mnist = keras.datasets.fashion_mnist.load_data()
+#vettori per trainare l'ia e per testarla
+(x_train,y_train),(x_test,y_test) = mnist
+
+import numpy as np
+
+#min = 0 e max = 255
+np.min(x_train), np.min(x_train)
+
+#categorie vestiti
+class_names = ['top', 'trouser', 'pullover', 'dress', ' coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
+
+#x_train e' una matrice e dobbiamo normalizzarlo (dividendolo per il max value)
+
+X_train = x_train/255.0
+X_test = x_test/255.0
+
+from keras import Sequential as Sequ
+from tensorflow.keras.layers import Flatten, Dense
+
+model = Sequ()
+#aggiungo il layer Flatten con la forma 28 x 28 pixel
+model.add(Flatten(input_shape=(28,28)))
+#aggiungo 128 neuroni
+model.add(Dense(128, activation='relu'))
+model.add(Dense(256, activation='relu'))
+#qui aggiungo 10 neuroni perche' abbiamo 10 categorie
+#con softmax activation -> perche' facciamo un multiclass classification (piu' di 2 classi)
+model.add(Dense(10, activation='softmax'))
+
+print(model.summary())
+
+#model compilation
+# loss function, optimazer, metrics
+
+#utilizziamo sparse_categorical_crossentropy perche'
+#il nostro output e' 0,1,2,3,4,5 -> sparse_categorical_crossentropy
+#il nostro output e' one hot encoded -> categorical_entropy
+# 0,1 -> binary_cross_entropy
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+#Passiamo i dati per il training e li dividiamo in 10 'epoche' passando 32 dati ogni epoca
+model_checkpoint_best_callback = keras.callbacks.ModelCheckpoint(
+    filepath = '../my_models/fashion_model.keras',
+    monitor = 'val_loss',
+    verbose = 1,
+    save_best_only = False,
+    save_weights_only = False
+)
+model.fit(X_train, y_train, epochs=10, batch_size=32, callbacks=[model_checkpoint_best_callback])
+

src/main.py

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+import tensorflow as tf
+import numpy as np
+
+(x_train,y_train),(x_test,y_test) = tf.keras.datasets.fashion_mnist.load_data()
+
+X_test = x_test/255.0
+
+model = tf.keras.models.load_model('my_models_h5/fashion_model.keras')
+
+model.evaluate(X_test, y_test)
+
+y_pred = model.predict(X_test)
+y_pred = np.argmax(y_pred, axis=1)
+
+from sklearn.metrics import classification_report
+#report per predire la accuracy
+print(classification_report(y_test, y_pred))
+