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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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60 lines
1.9 KiB
Python
60 lines
1.9 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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# Alright, so here have some code which should feel familiar from previous tutorials,
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# here is what we want to cover
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# 1. How to save and load model weights
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# 2. Save and loading entire model (Serializing model)
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# - Saves weights
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# - Model architecture
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# - Training Configuration (model.compile())
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# - Optimizer and states
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model1 = keras.Sequential([layers.Dense(64, activation="relu"), layers.Dense(10)])
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inputs = keras.Input(784)
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x = layers.Dense(64, activation="relu")(inputs)
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outputs = layers.Dense(10)(x)
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model2 = keras.Model(inputs=inputs, outputs=outputs)
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class MyModel(keras.Model):
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def __init__(self):
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super(MyModel, self).__init__()
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self.dense1 = layers.Dense(64, activation="relu")
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self.dense2 = layers.Dense(10)
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def call(self, input_tensor):
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x = tf.nn.relu(self.dense1(input_tensor))
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return self.dense2(x)
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# SavedModel format or HDF5 format
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model3 = MyModel()
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# model = keras.models.load_model('saved_model/')
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# model.load_weights('checkpoint_folder/')
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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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# model.save_weights('checkpoint_folder/')
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model.save("saved_model/")
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