35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import tensorflow as tf
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import numpy as np
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from tensorflow.keras import layers
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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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batch_size=64
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epochs=5
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(x_train, y_train), (x_test, y_test)=mnist.load_data()
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x_train=(x_train.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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x_test=(x_test.reshape(-1, 28, 28, 1)/255).astype(np.float32)
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train_ds=tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size)
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test_ds=tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)
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model = tf.keras.models.Sequential([
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layers.Conv2D(64, 3, strides=2, activation='relu'),
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layers.BatchNormalization(),
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layers.Conv2D(128, 3, strides=2, activation='relu'),
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layers.BatchNormalization(),
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layers.Flatten(),
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layers.Dense(512, activation='relu'),
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layers.BatchNormalization(),
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layers.Dense(10, activation='softmax')
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])
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.fit(x_train, y_train, epochs=epochs)
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#model.evaluate(x_test, y_test)
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