mirror of
https://github.com/jung-geun/PSO.git
synced 2025-12-20 04:50:45 +09:00
93 lines
2.2 KiB
Python
93 lines
2.2 KiB
Python
# %%
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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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gpus = tf.config.experimental.list_physical_devices("GPU")
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if gpus:
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try:
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tf.config.experimental.set_memory_growth(gpus[0], True)
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except Exception as e:
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print(e)
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finally:
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del gpus
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from keras.datasets import mnist
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from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
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from keras.models import Sequential
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from pso import Optimizer
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def get_data():
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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x_train = x_train.reshape((60000, 28, 28, 1))
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x_test = x_test.reshape((10000, 28, 28, 1))
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print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
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print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
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return x_train, y_train, x_test, y_test
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def get_data_test():
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_test = x_test.reshape((10000, 28, 28, 1))
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return x_test, y_test
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def make_model():
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model = Sequential()
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model.add(
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Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
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)
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model.add(MaxPooling2D(pool_size=(3, 3)))
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model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(128, activation="relu"))
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model.add(Dense(10, activation="softmax"))
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return model
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model = make_model()
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x_train, y_train, x_test, y_test = get_data()
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y_train = tf.one_hot(y_train, 10)
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y_test = tf.one_hot(y_test, 10)
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# model.compile(
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# optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
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# )
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model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])
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print("Training model...")
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model.fit(x_train, y_train, epochs=100, batch_size=128, verbose=1)
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print("Evaluating model...")
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model.evaluate(x_test, y_test, verbose=1)
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weights = model.get_weights()
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for w in weights:
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print(w.shape)
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print(w)
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print(w.min(), w.max())
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model.save_weights("weights.h5")
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# %%
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for w in weights:
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print(w.shape)
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print(w)
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print(w.min(), w.max())
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# %%
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