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79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import mlflow
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import mlflow.keras
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import numpy as np
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import warnings
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from tensorflow import keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import RMSprop
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print("Keras version:", keras.__version__)
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# Enable auto-logging to MLflow to capture Keras metrics.
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mlflow.autolog()
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# Model / data parameters
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n_inputs = 28 * 28
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n_h1 = 300
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n_h2 = 100
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n_outputs = 10
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learning_rate = 0.001
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# the data, split between train and test sets
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(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
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# Scale images to the [0, 1] range
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x_train = x_train.astype("float32") / 255
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x_test = x_test.astype("float32") / 255
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# Flatten image to be (n, 28 * 28)
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x_train = x_train.reshape(len(x_train), -1)
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x_test = x_test.reshape(len(x_test), -1)
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print("x_train shape:", x_train.shape)
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print(x_train.shape[0], "train samples")
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print(x_test.shape[0], "test samples")
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# convert class vectors to binary class matrices
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y_train = keras.utils.to_categorical(y_train, n_outputs)
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y_test = keras.utils.to_categorical(y_test, n_outputs)
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def driver():
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warnings.filterwarnings("ignore")
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with mlflow.start_run() as run:
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# Build a simple MLP model
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model = Sequential()
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# first hidden layer
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model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,)))
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# second hidden layer
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model.add(Dense(n_h2, activation='relu'))
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# output layer
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model.add(Dense(n_outputs, activation='softmax'))
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model.summary()
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batch_size = 128
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epochs = 5
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model.compile(loss='categorical_crossentropy',
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optimizer=RMSprop(lr=learning_rate),
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metrics=['accuracy'])
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model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
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score = model.evaluate(x_test, y_test, verbose=0)
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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return run
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if __name__ == "__main__":
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driver()
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