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11.py
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11.py
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import numpy as np
import matplotlib.pyplot as plt
import keyboard as kb
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Activation, Flatten
from keras.optimizers import SGD, Adam
from keras.datasets import fashion_mnist
def load_data():
(XtrainMat, Ytrain), (XtestMat, Ytest) = fashion_mnist.load_data()
n_train = len(Ytrain)
n_test = len(Ytest)
p_train = np.random.permutation(n_train)
p_test = np.random.permutation(n_test)
XtrainMat, Ytrain = XtrainMat[p_train] / 255, Ytrain[p_train]
XtestMat, Ytest = XtestMat[p_test] / 255, Ytest[p_test]
Xtrain = np.array([image.flatten() for image in XtrainMat])
Xtest = np.array([image.flatten() for image in XtestMat])
Xtrain = np.concatenate((np.ones((n_train, 1)), Xtrain), axis=1)
Xtest = np.concatenate((np.ones((n_test, 1)), Xtest), axis=1)
return Xtrain, Ytrain, Xtest, Ytest, XtrainMat, XtestMat
def build_model_1(lr=0.001):
model = Sequential()
model.add(Conv2D(16, (3, 3), padding='same',
activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Conv2D(8, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
sgd = SGD(lr=lr)
model.compile(optimizer=sgd, loss='sparse_categorical_crossentropy',
metrics=['acc'])
return model
def build_model_2(lr=0.001):
model = Sequential()
model.add(Conv2D(16, (3, 3), padding='same',
activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Conv2D(8, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
adam = Adam(lr=lr)
model.compile(optimizer=adam, loss='sparse_categorical_crossentropy',
metrics=['acc'])
return model
def train_model(model, Xtrain, Ytrain, bs=10, e=5):
train_hist = model.fit(Xtrain, Ytrain, batch_size=bs, epochs=e)
return (train_hist.history['loss'], train_hist.history['acc'])
def test_model(model, Xtest, Ytest):
loss, acc = model.test_on_batch(Xtest, Ytest)
return (loss, acc)
def test_lrs(Xtrain, Ytrain, lrs):
loss = []
accuracy = []
# create figure
fig, plots = plt.subplots(1, len(lrs))
fig.tight_layout()
# train models with lrs
for model, lr in enumerate(lrs):
print('\nLearning Rate:', lr)
new_loss, new_acc = train_model(build_model_1(lr), Xtrain, Ytrain)
loss.append(new_loss)
accuracy.append(new_acc)
# plot loss & accuracy
plots.ravel()[model].set_title(str(lr))
plots.ravel()[model].plot(loss[model], c='R')
plots.ravel()[model].plot(accuracy[model], c='G')
plt.show(fig)
def test_train_size(Xtrain, Ytrain, Xtest, Ytest, train_sizes):
loss = [[], []]
accuracy = [[], []]
for train_size in train_sizes:
# (re-)build models
models = [build_model_1(), build_model_2()]
# train & test models
print('\nTraining Set Size:', train_size)
for n, model in enumerate(models):
print(f"\nModel: {n + 1}")
train_model(model, Xtrain[:train_size], Ytrain[:train_size], e=10)
new_loss, new_acc = test_model(model, Xtest, Ytest)
loss[n].append(new_loss)
accuracy[n].append(new_acc)
# create figure
fig, plots = plt.subplots(1, 2)
fig.tight_layout()
# plot loss & accuracy
for model in range(2):
plots.ravel()[model].set_title(f"Model {model + 1}")
plots.ravel()[model].set_ylim(0, 1)
plots.ravel()[model].plot(train_sizes, loss[model], c='R')
plots.ravel()[model].plot(train_sizes, accuracy[model], c='G')
plt.show()
lrs = [1, 0.1, 0.01, 0.001, 0.0001]
train_sizes = [500, 2500, 15000, 30000]
Xtrain, Ytrain, Xtest, Ytest, XtrainMat, XtestMat = load_data()
# ----- 1 -----
# test_lrs(XtrainMat[:20000].reshape((-1, 28, 28, 1)), Ytrain[:20000], lrs)
# ----- 2 -----
# test_train_size(XtrainMat.reshape((-1, 28, 28, 1)), Ytrain,
# XtestMat.reshape((-1, 28, 28, 1)), Ytest, train_sizes)