def build_model_densenet_161():
img_input = Input(INPUT_SHAPE, name='data')
base_model = densenet161.DenseNet(
img_input=img_input,
reduction=0.5,
weights_path='../input/densenet161_weights_tf.h5',
classes=1000)
base_model.layers.pop()
base_model.layers.pop()
species_dense = Dense(len(SPECIES_CLASSES),
activation='softmax',
name='cat_species')(base_model.layers[-1].output)
cover_dense = Dense(len(COVER_CLASSES),
activation='softmax',
name='cat_cover')(base_model.layers[-1].output)
# output = concatenate([species_dense, cover_dense], axis=0)
model = Model(input=img_input, outputs=[species_dense, cover_dense])
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def build_model_densenet_with_mask():
img_input = Input(INPUT_SHAPE, name='data')
mask_model = model_unet(INPUT_SHAPE)
mask_model.load_weights(
'../output/checkpoints/fish_mask_unet/model_fish_unet2/checkpoint-best-064-0.0476.hdf5'
)
mask = mask_model(img_input)
mask3 = concatenate([mask, mask, mask], axis=3)
masked_image = multiply([img_input, mask3])
base_model = densenet161.DenseNet(
img_input=img_input,
reduction=0.5,
weights_path='../input/densenet161_weights_tf.h5',
classes=1000)
base_model.layers.pop()
base_model.layers.pop()
base_model_output = base_model(masked_image)
species_dense = Dense(len(SPECIES_CLASSES),
activation='softmax',
name='cat_species')(base_model_output)
cover_dense = Dense(len(COVER_CLASSES),
activation='softmax',
name='cat_cover')(base_model_output)
model = Model(inputs=img_input, outputs=[species_dense, cover_dense])
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def dense161():
weights_path = '/your path/wh_code/densenet/densenet161_weights_tf.h5'
image_size = (224, 224)
base_model = densenet161.DenseNet(reduction=0.5,
classes=1000,
weights_path=weights_path)
#base_model = densenet161.densenet161_model(img_rows=224, img_cols=224, color_type=3)
model = Model(base_model.input,
GlobalAveragePooling2D()(base_model.output))
gen = ImageDataGenerator(
preprocessing_function=densenet161.preprocess_input)
print(base_model.output)
#gen = ImageDataGenerator()
#train_generator = gen.flow_from_directory("/your path/train_cut2p", image_size, shuffle=False, batch_size=8)#37189
#train_generator = gen.flow_from_directory("/your path/traindata2_pre", image_size, shuffle=False, batch_size=8)#20227
#val_generator = gen.flow_from_directory("/your path/val_cutpre", image_size, shuffle=False, batch_size=8)#3117
#train_generator = gen.flow_from_directory("/your path/train", image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train_cut2p", image_size, shuffle=False, batch_size=8)#37189
#train_generator = gen.flow_from_directory("/your path/train_cut", image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train",image_size, shuffle=False, batch_size=8)#18686
#train_generator = gen.flow_from_directory("/your path/train_cut3p", image_size, shuffle=False, batch_size=8)#55298
test_generator = gen.flow_from_directory("/your path/test",
image_size,
shuffle=False,
batch_size=8,
class_mode=None) #10593
#test_generator = gen.flow_from_directory("/your path/test_cut", image_size, shuffle=False, batch_size=8, class_mode=None)#10593
#train_generator = gen.flow_from_directory("/your path/train4p", image_size, shuffle=False, batch_size=8)#73049
#train_generator = gen.flow_from_directory("/your path/train_cut4p", image_size, shuffle=False, batch_size=8)#72981
#train_generator = gen.flow_from_directory("/your path/train_cut5p", image_size, shuffle=False, batch_size=8)#90253
#train_generator = gen.flow_from_directory("/your path/train2p", image_size, shuffle=False, batch_size=8)#37201
train_generator = gen.flow_from_directory("/your path/train3p",
image_size,
shuffle=False,
batch_size=8) #55273
#train = model.predict_generator(train_generator,2336,verbose=True)#18686
#train = model.predict_generator(train_generator,4649,verbose=True)#37189
#train = model.predict_generator(train_generator,6913,verbose=True)#55298
#train = model.predict_generator(train_generator,6908,verbose=True)#55261
#val = model.predict_generator(val_generator, 390,verbose=True)#3117
#train = model.predict_generator(train_generator,4651,verbose=True)#37201
train = model.predict_generator(train_generator, 6910,
verbose=True) #55273
#train = model.predict_generator(train_generator,9127,verbose=True)#73014
#train = model.predict_generator(train_generator,9123,verbose=True)#72981
#train = model.predict_generator(train_generator,11282,verbose=True)#90253
test = model.predict_generator(test_generator, 1325, verbose=True) #10593
#print(test .shape)
with h5py.File(fileDir + "wh_code/nocut/224_3p-dense161.h5") as h:
h.create_dataset("train", data=train)
h.create_dataset("test", data=test)
h.create_dataset("label", data=train_generator.classes)
#h.create_dataset("val", data=val)
#h.create_dataset("trainlabel", data=train_generator.classes)
#h.create_dataset("vallabel", data=val_generator.classes)
K.clear_session()
from keras.models import *
from keras.layers import *
from keras.preprocessing.image import *
import h5py
import densenet161
from keras import backend as K
weights_path = '/your path/wh_code/densenet/densenet161_weights_tf.h5'
image_size = (224, 224)
base_model = densenet161.DenseNet(reduction=0.5,
classes=1000,
weights_path=weights_path)
model = Model(base_model.input, GlobalAveragePooling2D()(base_model.output))
gen = ImageDataGenerator(preprocessing_function=densenet161.preprocess_input)
test_generator = gen.flow_from_directory("/your path/test",
image_size,
shuffle=False,
batch_size=8,
class_mode=None)
train_generator = gen.flow_from_directory("/your path/train3p",
image_size,
shuffle=False,
batch_size=8)
train = model.predict_generator(train_generator, 6910, verbose=True)
test = model.predict_generator(test_generator, 1325, verbose=True)
# print(test .shape)
with h5py.File(fileDir + "wh_code/nocut/224_3p-dense161.h5") as h:
h.create_dataset("train", data=train)
h.create_dataset("test", data=test)
h.create_dataset("label", data=train_generator.classes)