python之keras 移动网络

以下是我的模型的架构。

# %% 
# Defining the model 
input_shape = img_data[0].shape 
 
model = Sequential() 
 
model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=input_shape)) 
model.add(Activation('relu')) 
 
model.add(Convolution2D(32, 3, 3)) 
model.add(Activation('relu')) 
 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Dropout(0.75)) 
 
model.add(Convolution2D(64, 3, 3)) 
model.add(Activation('relu')) 
# model.add(Convolution2D(64, 3, 3)) 
# model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Dropout(0.75)) 
 
model.add(Flatten()) 
model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.75)) 
model.add(Dense(num_classes)) 
model.add(Activation('softmax')) 
 
# sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) 
# model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=["accuracy"]) 
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=["accuracy"]) 

精度有点低。所以我想将架构转换为 mobilenet。是否有任何基于 keras 的实现来使用 mobilenet 对图像进行分类？

请您参考如下方法:

也许这段代码片段会对你有所帮助

from keras.applications.mobilenet import MobileNet 
from keras.applications.mobilenetv2 import MobileNetV2 
from  keras.preprocessing.image import ImageDataGenerator 
from keras.preprocessing import image 
from keras import Sequential 
from keras.layers import Dense 
from keras.optimizers import Adam, RMSprop, SGD 
import keras 
from tensorflow import confusion_matrix 
from matplotlib import pyplot as plt 
 
import config 
import numpy as np 
 
train_path = 'data/train' 
val_batch = 'data/val' 
test_batch = 'data/test' 
 
train_batches = ImageDataGenerator(preprocessing_function=keras.applications.mobilenet.preprocess_input).flow_from_directory(train_path, target_size=(config.IMAGE_SIZE, config.IMAGE_SIZE), 
                                                         class_mode='categorical', batch_size=20) 
val_batches = ImageDataGenerator(preprocessing_function=keras.applications.mobilenet.preprocess_input).flow_from_directory(val_batch, target_size=(config.IMAGE_SIZE, config.IMAGE_SIZE), 
                                                         class_mode='categorical', batch_size=20) 
 
def prepare_image(file): 
    img = image.load_img(file, target_size=(config.IMAGE_SIZE, config.IMAGE_SIZE)) 
    img_array = image.img_to_array(img) 
    img_expanded_dims = np.expand_dims(img_array, axis=0) 
    return keras.applications.mobilenet.preprocess_input(img_expanded_dims) 
 
mobilenet = MobileNetV2() 
 
# x =  mobilenet.layers[-6].output 
x =  mobilenet.layers[-2].output 
predictions =  Dense(8, activation='softmax')(x) 
from keras import Model 
model = Model(inputs= mobilenet.input, outputs=predictions) 
 
print(model.summary()) 
 
 
 
# for layer in model.layers[:-5]: 
#     layer.trainable = False 
 
 
# for layer in model.layers[:-1]: 
#     layer.trainable = False 
 
print(model.summary()) 
 
# exit(0) 
 
 
model.compile(SGD(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy']) 
 
history = model.fit_generator(train_batches, steps_per_epoch=10, 
                    validation_data=val_batches, validation_steps=10, epochs=300, verbose=2) 
 
acc = history.history['acc'] 
val_acc = history.history['val_acc'] 
loss = history.history['loss'] 
val_loss = history.history['val_loss'] 
 
epochs = range(len(acc)) 
 
plt.plot(epochs, acc, 'b', label='Training acc') 
plt.plot(epochs, val_acc, 'r', label='Validation acc') 
plt.title('Training and validation accuracy') 
plt.legend() 
 
plt.figure() 
 
plt.plot(epochs, loss, 'b', label='Training loss') 
plt.plot(epochs, val_loss, 'r', label='Validation loss') 
plt.title('Training and validation loss') 
plt.legend() 
 
plt.show() 
 
# Get the ground truth from generator 
ground_truth = train_batches.classes 
 
# Get the label to class mapping from the generator 
label2index = train_batches.class_indices 
 
# Getting the mapping from class index to class label 
idx2label = dict((v, k) for k, v in label2index.items()) 
 
print(idx2label) 
 
 
# _, val_labels =  next(val_batches) 
# 
# predictions = model.predict_generator(val_batches, steps=1, verbose=0) 
# 
# cm = confusion_matrix(val_batches, np.round(predictions[:,0])) 
# cm_plot_labels = [] 
# 
# for k, v in label2index.items(): 
#     cm_plot_labels.append(v) 
# 
# print(cm) 
 
 
 
# serialize model to JSON 
model_json = model.to_json() 
with open("mobilenet.json", "w") as json_file: 
    json_file.write(model_json) 
 
from keras.models import save_model 
save_model(model, 'mobilenet.h5') 
 
 
import tensorflow as tf 
# from tensorflow.contrib import lite 
# tf.lite.TocoConverter 
 
converter = tf.lite.TocoConverter.from_keras_model_file("mobilenet.h5") 
tflite_model = converter.convert() 
open("model/mobilenet.tflite", "wb").write(tflite_model)