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Author: MuhammadAzizulHakim

Article07 - TensorFlow/demo01_handwrittenDigitsClassifierFromScratch.py

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+import tensorflow as tf
+
+# Load and prepare the dataset
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# Build the tf.keras.Sequential model by stacking layers.
+# Choose an optimizer and loss function for training:
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10)
+])
+
+predictions = model(x_train[:1]).numpy()
+predictions
+
+# Convert the logits to probabilities for each class
+tf.nn.softmax(predictions).numpy()
+
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+
+loss_fn(y_train[:1], predictions).numpy()
+
+# Compile the deep learning model
+model.compile(optimizer='adam',
+              loss=loss_fn,
+              metrics=['accuracy'])
+
+# Fitting, adjust the model parameters to minimize the loss:
+model.fit(x_train, y_train, epochs=5)
+
+# Evaluate the model
+model.evaluate(x_test,  y_test, verbose=2)
+
+# Attach the softmax layer
+probability_model = tf.keras.Sequential([
+  model,
+  tf.keras.layers.Softmax()
+])
+
+probability_model(x_test[:5])