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models.py
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models.py
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# -*- coding: utf-8 -*-
#from __builtin__ import True
import numpy as np
import os
from keras.models import Sequential,Model
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers import LSTM, GlobalAveragePooling2D, GRU, Bidirectional, UpSampling2D
from keras.optimizers import SGD
from keras.applications import resnet50
from keras.layers import Flatten, Dense, Input
import matplotlib as plt
import keras.applications as kerasApp
from keras.utils import np_utils, plot_model
def mySpatialModel(model_name,spatial_size, nb_classes, channels, weights_path=None):
input_tensor = Input(shape=(channels, spatial_size, spatial_size))
input_shape = (channels,spatial_size, spatial_size)
base_model=None
predictions=None
data_dim=1024
if model_name=='ResNet50':
input_tensor = Input(shape=(spatial_size, spatial_size,channels))
input_shape = (spatial_size, spatial_size,channels)
base_model = kerasApp.ResNet50(include_top=False, input_tensor=input_tensor, input_shape=input_shape,
weights=weights_path, classes=nb_classes, pooling=None)
x = base_model.output
# 添加自己的全链接分类层 method 1
#x = Flatten()(x)
#predictions = Dense(nb_classes, activation='softmax')(x)
#method 2
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name=='VGG16':
input_tensor = Input(shape=(spatial_size,spatial_size,channels))
input_shape = (spatial_size, spatial_size,channels)
base_model = kerasApp.VGG16(include_top=False, input_tensor=input_tensor, input_shape=input_shape,weights=weights_path, classes=nb_classes, pooling=None)
x = base_model.output
x = GlobalAveragePooling2D()(x) # add a global spatial average pooling layer
x = Dense(1024, activation='relu')(x) # let's add a fully-connected layer
predictions = Dense(nb_classes, activation='softmax')(x) # and a logistic layer
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'VGG19':
base_model = kerasApp.VGG19(include_top=False, input_tensor=input_tensor, input_shape=input_shape,
weights=weights_path ,classes=2, pooling=None)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name=='InceptionV3':
input_tensor = Input(shape=( spatial_size, spatial_size,channels))
input_shape = (spatial_size, spatial_size,channels)
base_model = kerasApp.InceptionV3(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name=='InceptionResNetV2':
input_tensor = Input(shape=( spatial_size, spatial_size,channels))
input_shape = (spatial_size, spatial_size,channels,)
base_model = kerasApp.InceptionResNetV2(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
data_dim = 1536
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'Xception':
input_shape_xception = (spatial_size, spatial_size,channels)
base_model = kerasApp.Xception(weights=weights_path, include_top=False, pooling="avg",
input_shape=input_shape_xception, classes=nb_classes)
x = base_model.output
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet121':
base_model = kerasApp.DenseNet121(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet169':
base_model = kerasApp.DenseNet169(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet201':
base_model = kerasApp.DenseNet201(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'MobileNet':
base_model = kerasApp.MobileNet(weights=weights_path, include_top=False, pooling=None,
input_shape=input_shape, classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
data_dim=512
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
else:
print("this model--["+model_name+"]-- doesnt exist!")
# 冻结base_model所有层，这样就可以正确获得bottleneck特征
for layer in base_model.layers:
layer.trainable = True
# 训练模型
model = Model(inputs=base_model.input, outputs=predictions)
print('-------------当前base_model模型[' + model_name + "]-------------------\n")
print('base_model层数目:' + str(len(base_model.layers)))
print('model模型层数目:' + str(len(model.layers)))
featureLayer=model.layers[len(model.layers)-2]
print(featureLayer.output_shape)
print("data_dim:" + str(featureLayer.output_shape[1]))
print("---------------------------------------------\n")
#sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
# 绘制模型
#if plot_model:
# plot_model(model, to_file=model_name+'.png', show_shapes=True)
return model