Yuri Victorovich
26 lines
951 B
Python
26 lines
951 B
Python
import tensorflow as tf
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# 1. Load and prepare the MNIST dataset
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0 # Normalize pixel values
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# 2. Build the Sequential model
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)), # Flatten 2D image to 1D array
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tf.keras.layers.Dense(128, activation='relu'), # Hidden layer with ReLU activation
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tf.keras.layers.Dropout(0.2), # Regularization to prevent overfitting
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tf.keras.layers.Dense(10) # Output layer for 10 classes
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])
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# 3. Compile the model
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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# 4. Train the model
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model.fit(x_train, y_train, epochs=5)
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# 5. Evaluate accuracy
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model.evaluate(x_test, y_test, verbose=2)
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