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examples/demo_functional.py
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Matt Watson
Remove import keras as keras (#18725)
03 ноя 2023, 23:27
Не верифицирован
03 ноя 2023, 23:27
bc4308c
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import numpy as np from keras import Model from keras import layers from keras import losses from keras import metrics from keras import optimizers import keras keras.config.disable_traceback_filtering() inputs = layers.Input((100,)) x = layers.Dense(512, activation="relu")(inputs) residual = x x = layers.Dense(512, activation="relu")(x) x = layers.Dense(512, activation="relu")(x) x += residual x = layers.Dense(512, activation="relu")(x) residual = x x = layers.Dense(512, activation="relu")(x) x = layers.Dense(512, activation="relu")(x) x += residual residual = x x = layers.Dense(512, activation="relu")(x) x = layers.Dense(512, activation="relu")(x) x += residual outputs = layers.Dense(16)(x) model = Model(inputs, outputs) model.summary() x = np.random.random((50000, 100)) y = np.random.random((50000, 16)) batch_size = 32 epochs = 5 model.compile( optimizer=optimizers.Adam(learning_rate=0.001), loss=losses.MeanSquaredError(), metrics=[ metrics.CategoricalAccuracy(name="acc"), metrics.MeanSquaredError(name="mse"), ], ) print("\nTrain model") history = model.fit( x, y, batch_size=batch_size, epochs=epochs, validation_split=0.2 ) print("\nHistory:") print(history.history) print("\nEvaluate model") scores = model.evaluate(x, y, return_dict=True) print(scores) print("\nRun inference") pred = model.predict(x) print(f"Inferred output shape {pred.shape}")