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## 1. Import libs
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import numpy as np # linear algebra
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
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from keras.utils.np_utils import to_categorical # convert to one-hot-encoding
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
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"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile('cifar-10-python.tar.gz'):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
'cifar-10-python.tar.gz',
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open('cifar-10-python.tar.gz') as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
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%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 1
sample_id = 4
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
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def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
return x/255.0
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
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from sklearn import preprocessing
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
# TODO: Implement Function
lb = preprocessing.LabelBinarizer()
labels = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
lb.fit(labels)
one_hot = lb.transform(x)
return one_hot
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)
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"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
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"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
test_features, test_labels = pickle.load(open('preprocess_training.p', mode='rb'))
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
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print(test_features.shape)
print(test_labels.shape)
print(valid_features.shape)
print(valid_labels.shape)
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model = Sequential()
model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation='relu',
input_shape = (32, 32, 3)))
# model.add(BatchNormalization())
model.add(MaxPool2D(strides=(2,2)))
model.add(Conv2D(filters = 128, kernel_size = (3, 3), activation='relu'))
# model.add(BatchNormalization())
model.add(MaxPool2D(strides=(2,2)))
model.add(Conv2D(filters = 128, kernel_size = (3, 3), activation='relu'))
# model.add(BatchNormalization())
model.add(MaxPool2D(strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(10, activation='softmax'))
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datagen = ImageDataGenerator(zoom_range = 0.1,
height_shift_range = 0.1,
width_shift_range = 0.1,
rotation_range = 10)
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model.compile(loss='categorical_crossentropy', optimizer = Adam(lr=1e-4), metrics=["accuracy"])
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annealer = LearningRateScheduler(lambda x: 1e-3 * 0.9 ** x)
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hist = model.fit_generator(datagen.flow(test_features, test_labels, batch_size=16),
steps_per_epoch=500,
epochs=30, #Increase this when not on Kaggle kernel
verbose=2, #1 for ETA, 0 for silent
validation_data=(valid_features[:400,:], valid_labels[:400,:]), #For speed
callbacks=[annealer])
网络并不是单纯的网络单层节点越多越好，或者网络层数越多越好。一定的问题复杂度，需要一定规模的和结构的网络。
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final_loss, final_acc = model.evaluate(valid_features[400:], valid_labels[400:], verbose=0)
print("Final loss: {0:.4f}, final accuracy: {1:.4f}".format(final_loss, final_acc))
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plt.plot(hist.history['loss'], color='b')
plt.plot(hist.history['val_loss'], color='r')
plt.show()
plt.plot(hist.history['acc'], color='b')
plt.plot(hist.history['val_acc'], color='r')
plt.show()
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