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lenet.py
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import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Conv2D, AveragePooling2D, Flatten, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.metrics import sparse_categorical_accuracy
# mnist
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255
x_test = x_test.astype(np.float32) / 255
model = Sequential()
model.add(ZeroPadding2D(2, input_shape=(28, 28, 1)))
model.add(Conv2D(filters=6, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(units=120, activation=tf.nn.sigmoid))
model.add(Dense(units=84, activation=tf.nn.sigmoid))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.fit(x=x_train, y=y_train, epochs=10)
loss, acc = model.evaluate(x=x_test, y=y_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")
# mnist fashion
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train.astype(np.float32) / 255
x_test = x_test.astype(np.float32) / 255
model = Sequential()
model.add(ZeroPadding2D(2, input_shape=(28, 28, 1)))
model.add(Conv2D(filters=6, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(units=120, activation=tf.nn.sigmoid))
model.add(Dense(units=84, activation=tf.nn.sigmoid))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.fit(x=x_train, y=y_train, epochs=10)
loss, acc = model.evaluate(x=x_test, y=y_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")
# cifar10
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train = x_train.astype(np.float32) / 255
x_test = x_test.astype(np.float32) / 255
model = Sequential()
model.add(ZeroPadding2D(2, input_shape=(32, 32, 3)))
model.add(Conv2D(filters=6, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation=tf.nn.sigmoid))
model.add(AveragePooling2D())
model.add(Flatten())
model.add(Dense(units=120, activation=tf.nn.sigmoid))
model.add(Dense(units=84, activation=tf.nn.sigmoid))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.fit(x=x_train, y=y_train, epochs=10)
loss, acc = model.evaluate(x=x_test, y=y_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")