30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow import keras
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#load data set from tf.keras.datasets.mnist (Train number and Test number)
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mnist = tf.keras.datasets.mnist # 28x28 image of hand-written digits 0-9
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(x_train, y_train),(x_test, y_test) = mnist.load_data()
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# Build Model
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model = keras.Sequential([
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keras.layers.Flatten(input_shape=(28, 28)), # takes our 28x28 and makes it 1x784
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keras.layers.Dense(128, activation='relu'), # a simple fully-connected layer, 128 units, relu activation
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keras.layers.Dense(128, activation='relu'), # a simple fully-connected layer, 128 units, relu activation
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keras.layers.Dense(10, activation='softmax') # our output layer. 10 units for 10 classes. Softmax for probability distribution
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])
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# Compile model
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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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# Train the model
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model.fit(x_train, y_train, epochs=5)
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# Evaluate accuracy
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test_loss, test_acc = model.evaluate(x_test, y_test)
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print("Test loss:", test_loss)
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print("Test accuracy:", test_acc)
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# ***Save model***
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savepath = "num_reader.h5"
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model.save(savepath)
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