31 lines
745 B
Python
31 lines
745 B
Python
import numpy as np
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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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from keras_core.models import Functional
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inputs = layers.Input((128,), batch_size=32)
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x = layers.Dense(256)(inputs)
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x = layers.Dense(256)(x)
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x = layers.Dense(256)(x)
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outputs = layers.Dense(16)(x)
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model = Functional(inputs, outputs)
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model.summary()
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x = np.random.random((50000, 128))
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y = np.random.random((50000, 16))
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batch_size = 32
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epochs = 10
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model.compile(
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optimizer=optimizers.SGD(learning_rate=0.001),
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loss=losses.MeanSquaredError(),
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metrics=[metrics.MeanSquaredError()],
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)
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history = model.fit(x, y, batch_size=batch_size, epochs=epochs)
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print("History:")
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print(history.history)
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