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import tensorflow as tf | |
# Loading the data | |
mnist = tf.keras.datasets.mnist | |
(x_train,y_train),(x_test,y_test) = | |
mnist.load_data() | |
# Preprocessing - Normalization | |
x_train, x_test = x_train / 255.0, x_test / 255.0 | |
# Build the network | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Flatten(input_shape=(28, 28)), | |
tf.keras.layers.Dense(128, activation='relu'), | |
tf.keras.layers.Dense(16, activation='relu'), | |
tf.keras.layers.Dropout(0.2), | |
tf.keras.layers.Dense(10) | |
]) | |
# Define a loss function | |
loss_fn = | |
tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) | |
# Compile the model | |
model.compile(optimizer='adam',loss=loss_fn, | |
metrics=['accuracy']) | |
# training | |
model.fit(x_train, y_train, epochs=2) | |
# testing | |
model.evaluate(x_test,y_test) #0.98 | |
# inference | |
predictions = model(x_test[:1]).numpy() | |
tf.math.argmax(tf.nn.softmax(predictions).numpy()) |
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