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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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56 lines
1.7 KiB
Python
56 lines
1.7 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, regularizers
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from tensorflow.keras.datasets import cifar10
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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) = cifar10.load_data()
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x_train = x_train.astype("float32") / 255.0
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x_test = x_test.astype("float32") / 255.0
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def my_model():
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inputs = keras.Input(shape=(32, 32, 3))
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x = layers.Conv2D(32, 3, padding="same", kernel_regularizer=regularizers.l2(0.01),)(
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inputs
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)
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x = layers.BatchNormalization()(x)
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x = keras.activations.relu(x)
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x = layers.MaxPooling2D()(x)
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x = layers.Conv2D(64, 3, padding="same", kernel_regularizer=regularizers.l2(0.01),)(
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x
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)
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x = layers.BatchNormalization()(x)
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x = keras.activations.relu(x)
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x = layers.MaxPooling2D()(x)
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x = layers.Conv2D(
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128, 3, padding="same", kernel_regularizer=regularizers.l2(0.01),
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)(x)
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x = layers.BatchNormalization()(x)
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x = keras.activations.relu(x)
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x = layers.Flatten()(x)
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x = layers.Dense(64, activation="relu", kernel_regularizer=regularizers.l2(0.01),)(
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x
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)
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x = layers.Dropout(0.5)(x)
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outputs = layers.Dense(10)(x)
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model = keras.Model(inputs=inputs, outputs=outputs)
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return model
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model = my_model()
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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(lr=3e-4),
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metrics=["accuracy"],
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)
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model.fit(x_train, y_train, batch_size=64, epochs=150, verbose=2)
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model.evaluate(x_test, y_test, batch_size=64, verbose=2)
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