def RecognizeImg(model, mean, FileName = FLAGS.targetfile):
X = DirectReadImg(FileName)
X = du.featurewise_zero_center(X, mean)
pred = model.predict([X[0]])
print(pred)
return pred
def train(X_train_R, Y_train, X_test_R, Y_test, predict_data):
X_train_R, mean = du.featurewise_zero_center(X_train_R)
X_test_R = du.featurewise_zero_center(X_test_R, mean)
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
model = tflearn.DNN(net,
checkpoint_path='model_Digit',
max_checkpoints=2,
tensorboard_verbose=0,
tensorboard_dir='logs')
model.fit(X_train_R,
Y_train,
n_epoch=1,
validation_set=(X_test_R, Y_test),
show_metric=True,
batch_size=10,
run_id='Digit')
model.save("model_Digit/digit_recognition")
print("model train OK!\n save model OK!")
model.load("model_Digit/digit_recognition")
print("model already loaded!")
print("model predict")
label = model.predict(predict_data)
print("predict label: ", label)
def demo():
import tflearn.datasets.mnist as mnist
x, y, test_x, test_y = mnist.load_data(one_hot='True')
print(x.shape)
x = x.reshape([-1, 28, 28, 1])
test_x = test_x.reshape([-1, 28, 28, 1])
# 按功能划分的零中心将每个样本的中心置零，并指定平均值。如果未指定，则对所有样品评估平均值。
# Returns : A numpy array with same shape as input. Or a tuple (array, mean) if no mean value was specified.
x, mean = du.featurewise_zero_center(x)
test_x = du.featurewise_zero_center(test_x, mean)
net = tflearn.input_data(shape=[None, 28, 28, 1])
net = tflearn.conv_2d(net, 64, 3, activation='relu', bias=False)
net = tflearn.residual_bottleneck(net, 3, 16, 64)
net = tflearn.residual_bottleneck(net, 1, 32, 128, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 32, 128)
net = tflearn.residual_bottleneck(net, 1, 64, 256, downsample=True)
net = tflearn.residual_bottleneck(net, 2, 64, 256)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.1)
# Training
model = tflearn.DNN(net,
checkpoint_path='model_resnet_mnist',
max_checkpoints=10,
tensorboard_verbose=0)
model.fit(x,
y,
n_epoch=100,
validation_set=(test_x, test_y),
show_metric=True,
batch_size=256,
run_id='resnet_mnist')
def RecognizeImg(model, mean, FileName=FLAGS.targetfile):
imgsize = FLAGS.sample_size
if (FLAGS.modeltype == 1):
imgsize = 32
X = DirectReadImg(FileName,
Imgheight=imgsize,
Imgwidth=imgsize,
normlized=True)
X = du.featurewise_zero_center(X, mean)
pred = model.predict([X[0]])
print(pred)
return pred
trainx = []
path_train = []
for img in tqdm(os.listdir("C:/Users/aviga/Desktop/Train_5_letters")):
path = os.path.join("C:/Users/aviga/Desktop/Train_5_letters", img)
path_train.append(path)
sort_path_train = sorted(path_train, key=sort_internet_dataset)
for path in path_train:
img = Image.open(path)
img = resizeimage.resize_cover(img, [32, 32])
img = np.asarray(img)
trainx.append(img)
trainx = np.asarray(trainx)
trainx = trainx.reshape([-1, 32, 32, 1])
trainx, mean1 = du.featurewise_zero_center(trainx)
# train_y
trainy = pd.read_csv("C:/Users/aviga/Desktop/y_Train_5_letters.csv", header=None)
trainy = trainy.values.astype('int32') - 1
trainy = to_categorical(trainy, 30)
# train2
trainx2 = []
path_train_2 = []
for img in tqdm(os.listdir("C:/Users/aviga/Desktop/train")):
path = os.path.join("C:/Users/aviga/Desktop/train", img)
path_train_2.append(path)
sort_path_train_2 = sorted(path_train_2, key=sort_our_dataset)
for path in sort_path_train_2:
Links:
- [Deep Residual Network](http://arxiv.org/pdf/1512.03385.pdf)
- [CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html)
"""
from __future__ import division, print_function, absolute_import
import tflearn
import tflearn.data_utils as du
# Data loading
from tflearn.datasets import cifar10
(X, Y), (testX, testY) = cifar10.load_data()
# Data pre-processing
X, mean = du.featurewise_zero_center(X)
X, std = du.featurewise_std_normalization(X)
testX = du.featurewise_zero_center(testX, mean)
testX = du.featurewise_std_normalization(testX, std)
Y = du.to_categorical(Y, 10)
testY = du.to_categorical(testY, 10)
# Building Residual Network
net = tflearn.input_data(shape=[None, 32, 32, 3])
net = tflearn.conv_2d(net, 32, 3)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.shallow_residual_block(net, 4, 32, regularizer='L2')
net = tflearn.shallow_residual_block(net, 1, 32, downsample=True,
regularizer='L2')
net = tflearn.shallow_residual_block(net, 4, 64, regularizer='L2')
print('mean pickle is here!')
else:
# Load mean from original image files:
dataset_file = 'trainfilelist.txt'
# Build the preloader array, resize images to sample size
from tflearn.data_utils import image_preloader
X, Y = image_preloader(dataset_file, image_shape=(FLAGS.sample_size, FLAGS.sample_size),
mode='file', categorical_labels=True,
normalize=True)
#Reshape X
X = np.array(X)
X = X.reshape([-1, FLAGS.sample_size, FLAGS.sample_size, 1])
X, mean = du.featurewise_zero_center(X)
#write to pickle file
pkl_file = open('Inceptionmean.pkl', 'wb')
pickle.dump(mean, pkl_file)
pkl_file.close()
#load resnet model
network = create_Inception(3)
model = tflearn.DNN(network)
model.load(FLAGS.modelfile)
if(FLAGS.filemode):
RecognizeImg(model, mean, FLAGS.targetfile)
else:
DirectoryFileEnvaluation(model, mean, FLAGS.inputpath, 'jpeg')
