90 lines
2.2 KiB
Python
90 lines
2.2 KiB
Python
import numpy as np
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import keras_core
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from keras_core import layers
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from keras_core import operations as ops
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keras_core.utils.set_random_seed(1337)
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x = np.random.rand(100, 32, 32, 3)
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y = np.random.randint(0, 2, size=(100, 1))
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# Test sequential model.
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model = keras_core.Sequential(
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[
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layers.Conv2D(filters=10, kernel_size=3),
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layers.GlobalAveragePooling2D(),
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layers.Dense(1, activation="sigmoid"),
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]
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)
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model.compile(
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loss="binary_crossentropy", optimizer="adam", metrics=["mae", "accuracy"]
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)
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history = model.fit(
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x=x,
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y=y,
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epochs=10,
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validation_data=(x, y),
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verbose=0,
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)
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model.evaluate(x, y, verbose=0)
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model.predict(x, verbose=0)
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# Test functional model.
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inputs = keras_core.Input(shape=(32, 32, 3))
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outputs = layers.Conv2D(filters=10, kernel_size=3)(inputs)
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outputs = layers.GlobalAveragePooling2D()(outputs)
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outputs = layers.Dense(1)(outputs)
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model = keras_core.Model(inputs, outputs)
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model.compile(
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loss="binary_crossentropy", optimizer="adam", metrics=["mae", "accuracy"]
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)
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history = model.fit(
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x=x,
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y=y,
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epochs=10,
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validation_data=(x, y),
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verbose=0,
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)
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model.evaluate(x, y, verbose=0)
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model.predict(x, verbose=0)
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# Test custom layer
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class Linear(layers.Layer):
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def __init__(self, units=32):
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super().__init__()
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self.units = units
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def build(self, input_shape):
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self.w = self.add_weight(
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shape=(input_shape[-1], self.units),
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initializer="random_normal",
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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,), initializer="random_normal", trainable=True
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)
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def call(self, inputs):
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return ops.matmul(inputs, self.w) + self.b
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inputs = keras_core.Input(shape=(32, 32, 3))
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outputs = layers.Conv2D(filters=10, kernel_size=3)(inputs)
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outputs = layers.GlobalAveragePooling2D()(outputs)
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outputs = Linear(1)(outputs)
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outputs = layers.Activation("sigmoid")(outputs)
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model = keras_core.Model(inputs, outputs)
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model.compile(
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loss="binary_crossentropy", optimizer="adam", metrics=["mae", "accuracy"]
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)
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history = model.fit(
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x=x,
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y=y,
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epochs=10,
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validation_data=(x, y),
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verbose=0,
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)
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model.evaluate(x, y, verbose=0)
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model.predict(x, verbose=0)
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