43 lines
1004 B
Python
43 lines
1004 B
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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class MyModel(Model):
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def __init__(self, hidden_dim, output_dim):
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super().__init__()
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self.dense1 = layers.Dense(hidden_dim, activation="relu")
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self.dense2 = layers.Dense(hidden_dim, activation="relu")
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self.dense3 = layers.Dense(output_dim)
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def call(self, x):
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x = self.dense1(x)
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x = self.dense2(x)
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return self.dense3(x)
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model = MyModel(hidden_dim=256, output_dim=16)
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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 = 6
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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(
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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("History:")
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print(history.history)
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model.summary()
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