Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用

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章 オートエンコーダハンズオン

# Apply linear activation function to hidden layer

# Generate output layer with 29 nodes

model.add(Dense(units=29, activation='linear'))

# Compile the model

model.compile(optimizer='adam',

loss='mean_squared_error',

metrics=['accuracy'])

# Train the model

num_epochs = 10

batch_size = 32

history = model.fit(x=X_train_AE, y=X_train_AE,

epochs=num_epochs,

batch_size=batch_size,

shuffle=True,

validation_data=(X_train_AE, X_train_AE),

verbose=1)

# Evaluate on test set

predictions = model.predict(X_test, verbose=1)

anomalyScoresAE = anomalyScores(X_test, predictions)

preds, avgPrecision = plotResults(y_test, anomalyScoresAE, True) ...