简述
TensorFlow 包含图像识别的特殊功能,这些图像存储在特定文件夹中。使用相对相同的图像,出于安全目的,很容易实现此逻辑。
图像识别代码实现的文件夹结构如下图所示 -
dataset_image 包含需要加载的相关图片。我们将专注于图像识别,其中定义了我们的徽标。这些图像使用“load_data.py”脚本加载,这有助于记录其中的各种图像识别模块。
图像的训练有助于在指定文件夹中存储可识别的模式。
import numpy
import matplotlib.pyplot as plt
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
import load_data
from keras.models import Sequential
from keras.layers import Dense
import keras
K.set_image_dim_ordering('tf')
seed = 7
numpy.random.seed(seed)
(X_train,y_train) = load_data.data_set
X_train = X_train.astype('float32')
X_train = X_train / 255.0
y_train = np_utils.to_categorical(y_train)
num_classes = y_train.shape[1]
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), padding = 'same',
activation = 'relu', kernel_constraint = maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same',
kernel_constraint = maxnorm(3)))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(512, activation = 'relu', kernel_constraint = maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))
epochs = 10
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr = lrate, momentum = 0.9, decay = decay, nesterov = False)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
print(model.summary())
monitor = 'val_loss', min_delta = 0, patience = 0, verbose = 0, mode = 'auto')]
callbacks = [keras.callbacks.TensorBoard(log_dir='./logs',
histogram_freq = 0, batch_size = 32, write_graph = True, write_grads = False,
write_images = True, embeddings_freq = 0, embeddings_layer_names = None,
embeddings_metadata = None)]
model.fit(X_train, y_train, epochs = epochs,
batch_size = 32,shuffle = True,callbacks = callbacks)
scores = model.evaluate(X_train, y_train, verbose = 0)
print("Accuracy: %.2f%%" % (scores[1]*100))
model_json = model.to_json()
with open("model_face.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("model_face.h5")
print("Saved model to disk")
上面的代码行生成如下所示的输出 -