mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
synced 2026-02-21 11:18:01 +00:00
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.datasets import mnist
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# To Avoid GPU errors
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physical_devices = tf.config.list_physical_devices("GPU")
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tf.config.experimental.set_memory_growth(physical_devices[0], True)
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train = x_train.reshape(-1, 28 * 28).astype("float32") / 255.0
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x_test = x_test.reshape(-1, 28 * 28).astype("float32") / 255.0
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class Dense(layers.Layer):
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def __init__(self, units, input_dim):
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super(Dense, self).__init__()
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self.w = self.add_weight(
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name="w",
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shape=(input_dim, 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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name="b", shape=(units,), initializer="zeros", trainable=True
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)
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def call(self, inputs):
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return tf.matmul(inputs, self.w) + self.b
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class Dense(layers.Layer):
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def __init__(self, units):
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super(Dense, self).__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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name="w",
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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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name="b", 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 tf.matmul(inputs, self.w) + self.b
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class MyReLU(layers.Layer):
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def __init__(self):
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super(MyReLU, self).__init__()
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def call(self, x):
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return tf.math.maximum(x, 0)
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class MyModel(Model): # model.fit, model.evalute, model.predict
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def __init__(self, num_classes=10):
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super(MyModel, self).__init__()
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self.dense1 = Dense(64)
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self.dense2 = Dense(num_classes)
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self.relu = MyReLU()
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# self.dense1 = layers.Dense(64)
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# self.dense3 = layers.Dense(num_classes)
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def call(self, x):
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x = self.relu(self.dense1(x))
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return self.dense2(x)
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model = MyModel()
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model.compile(
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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optimizer=keras.optimizers.Adam(),
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metrics=["accuracy"],
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)
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model.fit(x_train, y_train, batch_size=32, epochs=2, verbose=2)
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model.evaluate(x_test, y_test, batch_size=32, verbose=2)
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