23-10-18

fashion mnist 추가
setup 조정

fashion_mnist.py

@@ -0,0 +1,138 @@
+# %%
+from pso import optimizer
+from tensorflow import keras
+from keras.models import Sequential
+from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
+from keras.datasets import mnist, fashion_mnist
+import tensorflow as tf
+import numpy as np
+import json
+import os
+import sys
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+
+def get_data():
+    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+    x_train = x_train.reshape((60000, 28, 28, 1))
+    x_test = x_test.reshape((10000, 28, 28, 1))
+
+    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
+
+    x_train, x_test = tf.convert_to_tensor(
+        x_train), tf.convert_to_tensor(x_test)
+    y_train, y_test = tf.convert_to_tensor(
+        y_train), tf.convert_to_tensor(y_test)
+
+    print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
+    print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
+
+    return x_train, y_train, x_test, y_test
+
+
+def get_data_test():
+    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+    x_test = x_test / 255.0
+    x_test = x_test.reshape((10000, 28, 28, 1))
+
+    y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
+
+    x_train, x_test = tf.convert_to_tensor(
+        x_train), tf.convert_to_tensor(x_test)
+    y_train, y_test = tf.convert_to_tensor(
+        y_train), tf.convert_to_tensor(y_test)
+
+    print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
+
+    return x_test, y_test
+
+
+def make_model():
+    model = Sequential()
+    model.add(
+        Conv2D(32, kernel_size=(5, 5), activation="sigmoid",
+               input_shape=(28, 28, 1))
+    )
+    model.add(MaxPooling2D(pool_size=(3, 3)))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation="sigmoid"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(128, activation="sigmoid"))
+    model.add(Dense(10, activation="softmax"))
+
+    return model
+
+
+def random_state():
+    with open(
+        "result/mnist/20230723-061626/mean_squared_error_[0.6384999752044678, 0.0723000094294548].json",
+        "r",
+    ) as f:
+        json_ = json.load(f)
+        rs = (
+            json_["random_state_0"],
+            np.array(json_["random_state_1"]),
+            json_["random_state_2"],
+            json_["random_state_3"],
+            json_["random_state_4"],
+        )
+
+    return rs
+
+
+# %%
+model = make_model()
+x_train, y_train, x_test, y_test = get_data()
+
+loss = [
+    "mean_squared_error",
+    "categorical_crossentropy",
+    "sparse_categorical_crossentropy",
+    "binary_crossentropy",
+    "kullback_leibler_divergence",
+    "poisson",
+    "cosine_similarity",
+    "log_cosh",
+    "huber_loss",
+    "mean_absolute_error",
+    "mean_absolute_percentage_error",
+]
+
+# rs = random_state()
+
+pso_mnist = optimizer(
+    model,
+    loss="mean_squared_error",
+    n_particles=500,
+    c0=0.2,
+    c1=0.4,
+    w_min=0.3,
+    w_max=0.5,
+    negative_swarm=0.05,
+    mutation_swarm=0.3,
+    particle_min=-4,
+    particle_max=4,
+)
+
+best_score = pso_mnist.fit(
+    x_train,
+    y_train,
+    epochs=200,
+    save_info=True,
+    log=2,
+    log_name="fashion_mnist",
+    save_path="./logs/fashion_mnist",
+    renewal="acc",
+    check_point=25,
+    empirical_balance=False,
+    dispersion=False,
+    batch_size=32,
+)
+
+print("Done!")
+
+sys.exit(0)

fashion_mnist_tf.py

@@ -0,0 +1,96 @@
+from keras.models import Sequential
+from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
+from keras.datasets import mnist, fashion_mnist
+from keras.utils import to_categorical
+# from tensorflow.data.Dataset import from_tensor_slices
+import tensorflow as tf
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+
+gpus = tf.config.experimental.list_physical_devices("GPU")
+if gpus:
+    try:
+        tf.config.experimental.set_memory_growth(gpus[0], True)
+    except Exception as e:
+        print(e)
+    finally:
+        del gpus
+
+
+def get_data():
+    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+    x_train = x_train.reshape((60000, 28, 28, 1))
+    x_test = x_test.reshape((10000, 28, 28, 1))
+
+    print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
+    print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
+
+    return x_train, y_train, x_test, y_test
+
+
+def get_data_test():
+    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+    x_test = x_test.reshape((10000, 28, 28, 1))
+
+    return x_test, y_test
+
+
+class _batch_generator:
+    def __init__(self, x, y, batch_size: int = 32):
+        self.batch_size = batch_size
+        self.index = 0
+        dataset = tf.data.Dataset.from_tensor_slices((x, y))
+        self.dataset = list(dataset.batch(batch_size))
+        self.max_index = len(dataset) // batch_size
+
+    def next(self):
+        self.index += 1
+        if self.index >= self.max_index:
+            self.index = 0
+        return self.dataset[self.index][0], self.dataset[self.index][1]
+
+
+def make_model():
+    model = Sequential()
+    model.add(
+        Conv2D(32, kernel_size=(5, 5), activation="relu",
+               input_shape=(28, 28, 1))
+    )
+    model.add(MaxPooling2D(pool_size=(3, 3)))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(128, activation="relu"))
+    model.add(Dense(10, activation="softmax"))
+
+    return model
+
+
+model = make_model()
+x_train, y_train, x_test, y_test = get_data()
+y_train = tf.one_hot(y_train, 10)
+y_test = tf.one_hot(y_test, 10)
+
+dataset = _batch_generator(x_train, y_train, 32)
+
+model.compile(optimizer="adam", loss="mse", metrics=["accuracy"])
+
+count = 0
+
+while count < 50:
+    x_batch, y_batch = dataset.next()
+    count += 1
+    print("Training model...")
+    model.fit(x_batch, y_batch, epochs=1, batch_size=1, verbose=1)
+
+print(count)
+
+print("Evaluating model...")
+model.evaluate(x_test, y_test, verbose=2)
+
+weights = model.get_weights()

setup.py

@@ -29,8 +29,8 @@ setup(
     license="MIT",
     classifiers=[
         "License :: OSI Approved :: MIT License",
-        "Programming Language :: Python :: 3:: Only"
+        "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.7"
+        "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",