wehub-resource-sync
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""Trains and evaluate a simple MLP
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on the Reuters newswire topic classification task.
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"""
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import numpy as np
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from tensorflow import keras
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from tensorflow.keras.datasets import reuters
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from tensorflow.keras.layers import Activation, Dense, Dropout
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.preprocessing.text import Tokenizer
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# The following import and function call are the only additions to code required
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# to automatically log metrics and parameters to MLflow.
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import mlflow
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mlflow.tensorflow.autolog()
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max_words = 1000
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batch_size = 32
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epochs = 5
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print("Loading data...")
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(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_words, test_split=0.2)
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print(len(x_train), "train sequences")
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print(len(x_test), "test sequences")
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num_classes = np.max(y_train) + 1
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print(num_classes, "classes")
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print("Vectorizing sequence data...")
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tokenizer = Tokenizer(num_words=max_words)
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x_train = tokenizer.sequences_to_matrix(x_train, mode="binary")
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x_test = tokenizer.sequences_to_matrix(x_test, mode="binary")
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print("x_train shape:", x_train.shape)
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print("x_test shape:", x_test.shape)
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print("Convert class vector to binary class matrix (for use with categorical_crossentropy)")
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y_train = keras.utils.to_categorical(y_train, num_classes)
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y_test = keras.utils.to_categorical(y_test, num_classes)
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print("y_train shape:", y_train.shape)
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print("y_test shape:", y_test.shape)
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print("Building model...")
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model = Sequential()
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model.add(Dense(512, input_shape=(max_words,)))
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model.add(Activation("relu"))
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model.add(Dropout(0.5))
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model.add(Dense(num_classes))
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model.add(Activation("softmax"))
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model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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history = model.fit(
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x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=0.1
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)
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score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
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print("Test score:", score[0])
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print("Test accuracy:", score[1])
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