37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
import tensorflow as tf
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import numpy as np
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from sklearn.datasets import load_iris
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data = load_iris()
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iris_data = np.float32(data.data)
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iris_target = np.float32(data.target)
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iris_target = np.float32(tf.keras.utils.to_categorical(iris_target, num_classes=3))
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train_data = tf.data.Dataset.from_tensor_slices((iris_data, iris_target)).batch(128)
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class MyLayer(tf.keras.layers.Layer):
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def __init__(self, output_dim):
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self.output_dim = output_dim
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super(MyLayer, self).__init__()
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def build(self, input_shape):
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self.weight = tf.Variable(tf.random.normal([input_shape[-1], self.output_dim]), name="dense_weight")
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self.bias = tf.Variable(tf.random.normal([self.output_dim]), name="bias_weight")
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super(MyLayer, self).build(input_shape) # Be sure to call this somewhere!
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def call(self, input_tensor):
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out = tf.matmul(input_tensor, self.weight) + self.bias
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out = tf.nn.relu(out)
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out = tf.keras.layers.Dropout(0.1)(out)
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return out
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input_xs = tf.keras.Input(shape=(4), name='input_xs')
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out = tf.keras.layers.Dense(32, activation='relu', name='dense_1')(input_xs)
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out = MyLayer(32)(out)
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out = MyLayer(48)(out)
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out = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(out)
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logits = tf.keras.layers.Dense(3, activation="softmax", name='predictions')(out)
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model = tf.keras.Model(inputs=input_xs, outputs=logits)
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print(model.summary())
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