90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
import numpy as np
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import keras
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from keras import Model
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from keras import initializers
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from keras import layers
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from keras import losses
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from keras import metrics
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from keras import ops
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from keras import optimizers
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class MyDense(layers.Layer):
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def __init__(self, units, name=None):
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super().__init__(name=name)
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self.units = units
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def build(self, input_shape):
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input_dim = input_shape[-1]
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self.w = self.add_weight(
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shape=(input_dim, self.units),
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initializer=initializers.GlorotNormal(),
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name="kernel",
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trainable=True,
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)
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self.b = self.add_weight(
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shape=(self.units,),
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initializer=initializers.Zeros(),
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name="bias",
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trainable=True,
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)
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def call(self, inputs):
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# Use Keras ops to create backend-agnostic layers/metrics/etc.
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return ops.matmul(inputs, self.w) + self.b
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class MyDropout(layers.Layer):
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def __init__(self, rate, name=None):
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super().__init__(name=name)
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self.rate = rate
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# Use seed_generator for managing RNG state.
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# It is a state element and its seed variable is
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# tracked as part of `layer.variables`.
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self.seed_generator = keras.random.SeedGenerator(1337)
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def call(self, inputs):
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# Use `keras.random` for random ops.
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return keras.random.dropout(
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inputs, self.rate, seed=self.seed_generator
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)
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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 = MyDense(hidden_dim)
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self.dense2 = MyDense(hidden_dim)
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self.dense3 = MyDense(output_dim)
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self.dp = MyDropout(0.5)
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def call(self, x):
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x1 = self.dense1(x)
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x2 = self.dense2(x)
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# Why not use some ops here as well
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x = ops.concatenate([x1, x2], axis=-1)
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x = self.dp(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 = 5
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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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model.summary()
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print("History:")
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print(history.history)
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