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keras_optimizers.py
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keras_optimizers.py
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import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, Activation
from keras.callbacks import ModelCheckpoint
from keras.callbacks import TensorBoard
from keras.callbacks import LearningRateScheduler
from keras.callbacks import EarlyStopping
import math
import matplotlib.pyplot as plt
tf.keras.utils.set_random_seed(42)
SAVE_PATH = "/content/drive/MyDrive/Colab Notebooks/data/"
def scheduler(epoch, lr):
if epoch < 4:
return lr
else:
return lr * tf.math.exp(-0.1)
if __name__ == "__main__":
img_rows, img_cols = 32, 32
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3).astype("float32") / 255
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3).astype("float32") / 255
y_train_label = keras.utils.to_categorical(y_train)
y_test_label = keras.utils.to_categorical(y_test)
num_classes = y_train_label.shape[1]
#training parameters
batch_size = 256
num_epochs = 32
#model parameters
num_filters_l1 = 64
num_filters_l2 = 128
#CNN architecture
cnn = Sequential()
cnn.add(Conv2D(num_filters_l1, kernel_size = (5, 5), input_shape=(img_rows, img_cols, 3), padding='same'))
cnn.add(Activation('relu'))
cnn.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
cnn.add(Conv2D(num_filters_l2, kernel_size = (5, 5), padding='same'))
cnn.add(Activation('relu'))
cnn.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
cnn.add(Flatten())
cnn.add(Dense(128))
cnn.add(Activation('relu'))
cnn.add(Dense(num_classes))
cnn.add(Activation('softmax'))
#optimizers
opt1 = tf.keras.optimizers.SGD()
opt2 = tf.keras.optimizers.SGD(momentum=0.9, nesterov=True)
opt3 = tf.keras.optimizers.RMSprop()
opt4 = tf.keras.optimizers.Adam()
optimizer_list = [opt1, opt2, opt3, opt4]
history_list = []
for idx in range(len(optimizer_list)):
K.clear_session()
opt = optimizer_list[idx]
cnn.compile(
loss=keras.losses.CategoricalCrossentropy(),
optimizer=opt,
metrics=["accuracy"]
)
#define callbacks
reduce_lr = LearningRateScheduler(scheduler, verbose=1)
callbacks_list = [reduce_lr]
#training loop
hist = cnn.fit(x_train, y_train_label, batch_size=batch_size, epochs=num_epochs, callbacks=callbacks_list, validation_split=0.2)
history_list.append(hist)
#end for
plt.figure()
plt.plot(history_list[0].history['loss'], 'b', lw=2.0, label='SGD')
plt.plot(history_list[1].history['loss'], '--r', lw=2.0, label='SGD Nesterov')
plt.plot(history_list[2].history['loss'], ':g', lw=2.0, label='RMSProp')
plt.plot(history_list[3].history['loss'], '-.k', lw=2.0, label='ADAM')
plt.title('LeNet, CIFAR-100, Optimizers')
plt.xlabel('Epochs')
plt.ylabel('Cross-Entropy Training Loss')
plt.legend(loc='upper right')
plt.show()
#plt.savefig('./figures/lenet_loss.png')
plt.figure()
plt.plot(history_list[0].history['val_accuracy'], 'b', lw=2.0, label='SGD')
plt.plot(history_list[1].history['val_accuracy'], '--r', lw=2.0, label='SGD Nesterov')
plt.plot(history_list[2].history['val_accuracy'], ':g', lw=2.0, label='RMSProp')
plt.plot(history_list[3].history['val_accuracy'], '-.k', lw=2.0, label='ADAM')
plt.title('LeNet, CIFAR-100, Optimizers')
plt.xlabel('Epochs')
plt.ylabel('Validation Accuracy')
plt.legend(loc='upper right')
plt.show()
#plt.savefig('./figures/lenet_loss.png')
plt.figure()
plt.plot(history_list[0].history['lr'], 'b', lw=2.0, label='SGD')
plt.plot(history_list[1].history['lr'], '--r', lw=2.0, label='SGD Nesterov')
plt.plot(history_list[2].history['lr'], ':g', lw=2.0, label='RMSProp')
plt.plot(history_list[3].history['lr'], '-.k', lw=2.0, label='ADAM')
plt.title('LeNet, CIFAR-100, Optimizers')
plt.xlabel('Epochs')
plt.ylabel('Learning Rate Schedule')
plt.legend(loc='upper right')
plt.show()
#plt.savefig('./figures/lenet_loss.png')