53 lines
1.2 KiB
Python
53 lines
1.2 KiB
Python
import numpy as np
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from keras_core import Model
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from keras_core import layers
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from keras_core import losses
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from keras_core import metrics
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from keras_core import optimizers
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inputs = layers.Input((100,))
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x = layers.Dense(512, activation="relu")(inputs)
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residual = x
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x = layers.Dense(512, activation="relu")(x)
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x = layers.Dense(512, activation="relu")(x)
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x += residual
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x = layers.Dense(512, activation="relu")(x)
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residual = x
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x = layers.Dense(512, activation="relu")(x)
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x = layers.Dense(512, activation="relu")(x)
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x += residual
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outputs = layers.Dense(16)(x)
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model = Model(inputs, outputs)
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model.summary()
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x = np.random.random((50000, 100))
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y = np.random.random((50000, 16))
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batch_size = 32
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epochs = 5
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model.compile(
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optimizer=optimizers.Adam(learning_rate=0.001),
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loss=losses.MeanSquaredError(),
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metrics=[
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metrics.CategoricalAccuracy(name="acc"),
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metrics.MeanSquaredError(name="mse"),
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],
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)
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print("\nTrain model")
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history = model.fit(
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x, y, batch_size=batch_size, epochs=epochs, validation_split=0.2
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)
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print("\nHistory:")
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print(history.history)
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print("\nEvaluate model")
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scores = model.evaluate(x, y, return_dict=True)
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print(scores)
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print("\nRun inference")
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pred = model.predict(x)
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print(f"Inferred output shape {pred.shape}")
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