Question

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"])

准确度有点低。所以我想将架构转移到mobilenet。有没有基于keras的实现来使用mobilenet对图像进行分类？

Answer 1

Keras有一套用于图像分类目的的预训练模型。您可以查看列表和使用情况here

您还可以在github存储库here the link

上复制架构的实现

Answer 2

此代码片段可能会对您有所帮助

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)

keras的移动网络

2 个答案: