TensorFlow图像识别

TensorFlow包含图像识别的特殊功能，并且这些图像存储在特定的文件夹中，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像。无涯教程将专注于图像识别，图像使用" load_data.py"脚本加载，这有助于在其中的各个图像识别模块上保留注释。

import pickle
from sklearn.model_selection import train_test_split
from scipy import misc

import numpy as np
import os

label=os.listdir("dataset_image")
label=label[1:]
dataset=[]

for image_label in label:
   images=os.listdir("dataset_image/"+image_label)
   
   for image in images:
      img=misc.imread("dataset_image/"+image_label+"/"+image)
      img=misc.imresize(img, (64, 64))
      dataset.append((img,image_label))
X=[]
Y=[]

for input,image_label in dataset:
   X.append(input)
   Y.append(label.index(image_label))

X=np.array(X)
Y=np.array(Y)

X_train,y_train,=X,Y

data_set=(X_train,y_train)

save_label=open("int_to_word_out.pickle","wb")
pickle.dump(label, save_label)
save_label.close()

图像训练有助于将可识别的图案存储在指定的文件夹中。

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')

# fix random seed for reproducibility
seed=7
numpy.random.seed(seed)

# load data
(X_train,y_train)=load_data.data_set

# normalize inputs from 0-255 to 0.0-1.0
X_train=X_train.astype('float32')

#X_test=X_test.astype('float32')
X_train=X_train/255.0

#X_test=X_test/255.0
# one hot encode outputs
y_train=np_utils.to_categorical(y_train)

#y_test=np_utils.to_categorical(y_test)
num_classes=y_train.shape[1]

# Create the model
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'))

# Compile model
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())

#callbacks=[keras.callbacks.EarlyStopping(
   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)]

# Fit the model

model.fit(X_train, y_train, epochs=epochs, 
   batch_size=32,shuffle=True,callbacks=callbacks)

# Final evaluation of the model
scores=model.evaluate(X_train, y_train, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

# serialize model to JSONx
model_json=model.to_json()
with open("model_face.json", "w") as json_file:
   json_file.write(model_json)

# serialize weights to HDF5
model.save_weights("model_face.h5")
print("Saved model to disk")