def load_model(config):
alexnet = convnet('alexnet', weights_path=config['pre_weight_path'])
input = alexnet.input
img_representation = alexnet.get_layer("flatten").output
#print img_representation
#dense_3 = Dropout(0.5)(dense_2)
#dense_3 = Dense(1000,name='dense_3')(dense_3)
#prediction = Activation("softmax",name="softmax")(dense_3)
#classifier = Flatten(name="flatten")(img_representation)
classifier = Dense(4096, activation="relu",
name='dense_1')(img_representation)
classifier = Dropout(0.5)(classifier)
classifier = Dense(4096, activation="relu", name='dense_2')(classifier)
classifier = Dropout(0.5)(classifier)
classifier = Dense(5, name='dense_3')(classifier)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input, output=classifier)
# Uncomment below to set the first 10 layers to non-trainable (weights will not be updated)
print("Number of Layers: {}".format(len(model.layers)))
for idx, layer in enumerate(model.layers):
if idx < 29:
layer.trainable = False
else:
print("Layer {} is trainable".format(idx))
print("input shape {}".format(layer.input_shape))
print("output shape {}".format(layer.output_shape))
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 init_xception(lr, hidden, drop):
lr = 1 / np.power(10, lr)
"""base_xception = tf.keras.applications.Xception(input_shape=(img_height, img_width, channels),
include_top=False, weights="imagenet")
x = base_xception.output
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dropout(drop)(x)
x = tf.keras.layers.Dense(hidden, activation="elu")(x)
output_layer = tf.keras.layers.Dense(1)(x)
xception_model = tf.keras.Model(inputs=base_xception.input, outputs=output_layer)
xception_model.compile(tf.keras.optimizers.Adam(lr=lr), tf.keras.losses.MeanSquaredError(), ["mae", "accuracy"])"""
base_alexnet = convnet("alexnet", weights_path="alexnet_weights.h5", heatmap=False)
x = base_alexnet.output
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dropout(drop)(x)
x = tf.keras.layers.Dense(hidden, activation="elu")
output_layer = tf.keras.layers.Dense(1)(x)
alexnet = tf.keras.Model(inputs=base_alexnet.input, outputs=output_layer)
alexnet.compile(tf.keras.optimizers.Adam(lr=lr), tf.keras.losses.MeanSquaredError(), ["mae", "accuracy"])
return alexnet
def __init__(self, weights_path=None):
self.img_width, self.img_height = 64, 64 # dimensions of our images.
self.out_width, self.out_height = 150, 350 # dimensions of our images.
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
self.model = convnet('alexnet',weights_path=weights_path, heatmap=True)
#model = convnet('vgg_16', weights_path="post_vgg16_weights.h5", heatmap=True)
self.model.compile(optimizer=sgd, loss='mse')
def __init__(self):
K.set_image_dim_ordering('th')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
#self.model = convnet('alexnet', weights_path="alexnet_weights_imagenet.h5", heatmap=False)
self.model = convnet(
'alexnet',
weights_path="./networks/alexnet/alexnet_weights_imagenet.h5",
heatmap=False)
self.model.compile(optimizer=sgd, loss='mse')
def __init__(self, weights_path=None):
self.img_width, self.img_height = 64, 64 # dimensions of our images.
self.out_width, self.out_height = 150, 350 # dimensions of our images.
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
print("loading alexnet weights from: {}".format(weights_path))
self.model = convnet('alexnet',
weights_path=weights_path,
heatmap=True)
#model = convnet('vgg_16', weights_path="post_vgg16_weights.h5", heatmap=True)
print("compiling model..")
self.model.compile(optimizer=sgd, loss='categorical_crossentropy')
def alex_model(config):
alexnet = convnet('alexnet', weights_path=config['pre_weight_path'])
input = alexnet.input
img_representation = alexnet.get_layer("dense_2").output
classifier = Dense(4,name='dense_3')(img_representation)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input,output=classifier)
# Uncomment below to set the first 10 layers to non-trainable (weights will not be updated)
for layer in model.layers[:14]:
layer.trainable = False
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 extract_features(files, path, labels, num_batches=1, net='alexnet'):
batches = split_list(files, wanted_parts=num_batches)
size = len(batches)
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
if net == 'alexnet':
wp = "../weights/alexnet_weights.h5"
sc = [227, 227, 3]
elif net == 'vgg_16':
wp = "../weights/vgg16_weights.h5"
sc = [224, 224, 3]
elif net == 'vgg_19':
wp = "../weights/vgg19_weights.h5"
sc = [224, 224, 3]
else:
print('Wrong network!')
return 0
model = convnet(net, weights_path=wp, heatmap=False)
model.compile(optimizer=sgd, loss='mse')
feature_map = Model(input=model.input,
output=model.get_layer('dense_2').output)
y = []
n = 0
print('Starting, done: ' + str(n) + '%')
times = [time.time()]
for names in batches:
n += 1
for case in names:
y.append(labels[case[:-4] + '.aiff'])
actual_batch = get_images_ready_alexnet_batch(names, path, size=sc)
features = feature_map.predict(actual_batch)
if n == 1:
data = features
else:
data = numpy.concatenate((data, features))
if n * 100 / size % 5 == 0:
times.append(time.time())
print('Done for: ' + str(n * 100 / size) + '%')
print('Time for the last 5%: ' + str(times[-1] - times[-2]))
print('Expected time to finish: ' +
str((20 - n * 20 / size) *
(times[-1] - times[-2]) / 60) + ' minutes.')
print('Done.')
return data, y
def load_model(model_name):
print("Loading {} model...".format(model_name))
if model_name == "resnet50":
model = ResNet50(weights='imagenet')
elif model_name == "inceptionv3":
model = InceptionV3(weights='imagenet')
elif model_name == "vgg19":
model = VGG19(weights='imagenet')
elif model_name == "vgg16":
model = VGG16(weights='imagenet')
elif model_name == "alexnet":
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',
weights_path="../alexnet/alexnet_weights.h5",
heatmap=False)
model.compile(optimizer=sgd, loss='mse')
else:
raise ValueError("Wrong model name")
print("Loaded!")
return model, model_name
from keras.optimizers import SGD
from convnetskeras.convnets import preprocess_image_batch, convnet
im = preprocess_image_batch(['examples/index.jpg'],
img_size=(256, 256),
crop_size=(227, 227),
color_mode="rgb")
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',
weights_path="weights/alexnet_weights(1).h5",
heatmap=False)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
model = Model(input=inputs, output=dense_1)
if weights_path:
model.load_weights(weights_path)
return model
#Here is code from a 3D CNN example on the following blog:
# http://learnandshare645.blogspot.in/2016/06/3d-cnn-in-keras-action-recognition.html
#
#Good initial CNN tutorial:
# http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/
#ourAlexModel = convnet('alexnet',weights_path='alexnet_weights.h5')
ourAlexModel = myAlexNet()
originalAlexModel = convnet('alexnet',
weights_path='alexnet_weights.h5',
heatmap=False)
#alexmodel = convnet('alexnet', heatmap=False)
for layer, mylayer in zip(originalAlexModel.layers, ourAlexModel.layers):
print(layer.name)
#if mylayer.name == 'mil_1':
if mylayer.name == 'flatten':
break
else:
weightsval = layer.get_weights()
print(len(weightsval))
mylayer.set_weights(weightsval)
ourAlexModel.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
print(np.sum(y1 == 1))
print(np.sum(y1 == 2))
print(np.sum(y1 == 3))
print(np.sum(y1 == 4))
# print(GL)
print(GL.shape[0])
print(TOT_0_r.shape[0])
#########################################################
#### CNN feature extraction
#########################################################
#CNN_model = convnet('alexnet',weights_path="alexnet_weights.h5", heatmap=False)
CNN_model = convnet('alexnet24',
weights_path="./tmp/weights_alex_7_tot.hdf5",
heatmap=False)
# CNN_model = convnet('vgg_16',weights_path="vgg16_weights.h5", heatmap=False)
from keras import backend as K
get_dense_features = K.function(
[CNN_model.layers[0].input, K.learning_phase()],
[CNN_model.layers[31].output]) #33
print("Feature extraction...")
X_0 = np.zeros((97, INPUT_LEN, INPUT_DIM))
X_90 = np.zeros((97, INPUT_LEN, INPUT_DIM))
X_180 = np.zeros((97, INPUT_LEN, INPUT_DIM))
X_270 = np.zeros((97, INPUT_LEN, INPUT_DIM))
y = np.zeros((97, INPUT_LEN, OUTPUT_LEN))
# print 'This Breed is not served, or could not be classified'
import numpy as np
from keras.optimizers import SGD
from keras.preprocessing import image
from convnetskeras.convnets import preprocess_image_batch, convnet
import sys
############################################# MODEL
# Optimizer
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
# Model
model = convnet(
'alexnet',
weights_path=
"/home/mansoor/pywork/catsanddogs/Alexnet/SGD_CC/allbest/weights--11-0.94.hdf5",
heatmap=False,
trainable=False)
# Compile model
model.compile(optimizer=sgd, loss='categorical_crossentropy')
print "Model successfully built."
def process_image(img_path):
########################################### Predictions
print img_path
img = image.load_img(img_path, target_size=(227, 227))
x = image.img_to_array(img)
"teapot", "table lamp", "castle", "pillow", "volcano", "coffee mug",
"envelope"
]
categoryPaths = [[
'data/' + category + '/0000000' + str(i) + '.jpg' for i in range(500)
] for category in categories]
outputs = []
for category, paths in enumerate(categoryPaths):
print('start processing category ' + categories[category])
# And resize it to fit
imgs = preprocess_image_batch(paths,
img_size=(256, 256),
crop_size=(224, 224),
color_mode="bgr")
# Specify Model Parameters, load pretrained weights and compile Model
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('vgg_16', weights_path="vgg16_weights.h5", heatmap=False)
model.compile(optimizer=sgd, loss='mse')
# Predict image
out = model.predict(imgs)
print('saving')
json.dump({categories[category]: out.tolist()},
open(categories[category] + '.json', 'w'))
outputs.append({categories[category]: out.tolist()})
print('save final result')
json.dump(outputs, open('classifications.json', 'w'))
print('finished')
im = im[:,:,0:3]
#print(im.shape)
draw_img[0:im.shape[0], 0:im.shape[1] ] = im
return draw_img
from keras.optimizers import SGD
from convnetskeras.convnets import preprocess_image_batch, convnet
from convnetskeras.imagenet_tool import synset_to_dfs_ids
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',weights_path="weights/alexnet_weights.h5", heatmap=True)
model.compile(optimizer=sgd, loss='mse')
def full_pipe(img):
img = lanefind.lanePipeline(img)
img = carDetectionPipelineNN(img)
return img
from moviepy.editor import VideoFileClip
def processVideo(input_video,output):
initializePipeline()
print('input:',input_video)
clip1 = VideoFileClip(input_video)
print('got clip1')
#out_clip = clip1.fl_image(carDetectionPipelineNN)
out_clip = clip1.fl_image(full_pipe)
from keras.optimizers import SGD
from keras.regularizers import l2
from keras.models import Model
from keras.layers import Dense, Dropout, Input, Activation
from convnetskeras. convnets import convnet
trainable_layers = [None]
vgg16 = convnet('vgg_16',
weights_path='/srv/data/convnetsweights/vgg16_weights.h5',
trainable=["None"])
for l in vgg16.layers:
if l.name in trainable_layers:
pass
else:
l.trainable = False
input = vgg16.input
img_representation = vgg16.get_layer("dense_2").output
classifier = Dense(2,name='classifier',
W_regularizer=l2(0.001))(img_representation)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input,output=classifier)
X_test_extend = np.zeros((X_test.shape[0], 3, 227, 227))
for i in xrange(X_test.shape[0]):
rex = np.resize(X_test[i, :, :, :], (227, 227))
X_test_extend[i, 0, :, :] = rex
X_test_extend[i, 1, :, :] = rex
X_test_extend[i, 2, :, :] = rex
X_test = X_test_extend
X_test_test_extend = np.zeros((X_test_test.shape[0], 3, 227, 227))
for i in xrange(X_test_test.shape[0]):
rex = np.resize(X_test_test[i, :, :, :], (227, 227))
X_test_test_extend[i, 0, :, :] = rex
X_test_test_extend[i, 1, :, :] = rex
X_test_test_extend[i, 2, :, :] = rex
X_test_test = X_test_test_extend
alexmodel = convnet('alexnet',
weights_path='alexnet_weights.h5',
heatmap=False)
model = convnet('alexnet', outdim=2)
if pretrain:
for layer, mylayer in zip(alexmodel.layers, model.layers):
print(layer.name)
if layer.name == 'dense_3':
break
else:
weightsval = layer.get_weights()
print(len(weightsval))
mylayer.set_weights(weightsval)
X_test_test = X_test_test.astype('float32')
for f in os.listdir('./'):
metrics = ['loss', 'auc', 'f1', 'acc', 'reca', 'prec']
from keras.optimizers import SGD
from keras.regularizers import l2
from keras.models import Model
from keras.layers import Dense, Dropout, Input, Activation
from convnetskeras.convnets import convnet
trainable_layers = []
vgg16 = convnet('vgg_16',
weights_path='/srv/data/convnetsweights/vgg16_weights.h5',
trainable=["None"])
for l in vgg16.layers:
if l.name in trainable_layers:
pass
else:
l.trainable = False
input = vgg16.input
img_representation = vgg16.get_layer("flatten").output
classifier = Dense(2, name='classifier',
W_regularizer=l2(0.001))(img_representation)
classifier = Activation("softmax", name="softmax")(classifier)
model = Model(input=input, output=classifier)
sgd = SGD(lr=.1, decay=1.e-6, momentum=0.9, nesterov=False)
model.compile(optimizer=sgd,
# just_file_names.append(each.split('/')[8])
#targets = np.concatenate((np.ones((len(image_paths_live),3,227,227)),np.zeros((len(image_paths_spoof),3,227,227))))
#targets = np.array([list(np.concatenate((np.ones(len(image_paths_live)),np.zeros(len(image_paths_spoof))))).reshape(len(image_paths_live)+len(image_paths_spoof),1), list(np.concatenate((np.zeros(len(image_paths_live)),np.ones(len(image_paths_spoof))))).reshape(len(image_paths_live)+len(image_paths_spoof),1)])
im = preprocess_image_batch(im_full_paths,
img_size=(256, 256),
crop_size=(227, 227),
color_mode="rgb")
im_validation = preprocess_image_batch(im_full_paths_validation,
img_size=(256, 256),
crop_size=(227, 227),
color_mode="rgb")
base_model = convnet(
'alexnet',
weights_path=
"/Users/km4n6/Box Sync/kiran/NN_project/final_project/weights/alexnet_weights.h5",
heatmap=False)
x = base_model.output
#x=Dropout(0.5)(x)
x = Dense(550,
input_dim=500,
name='Relu_dense',
init='normal',
activation='relu')(x)
x = Dense(250,
input_dim=250,
name='Relu_dense2',
init='normal',
activation='relu')(x)
predictions = Dense(2,
dgdx_val = test_datagen.flow_from_directory(
'/home/wangnxr/dataset/vid_offset_0/test/',
read_formats={'png'},
num_frames=4,
target_size=(int(340), int(256)),
batch_size=32,
class_mode='binary')
#pdb.set_trace()
train_datagen.fit_generator(dgdx, nb_iter=96)
test_datagen.fit_generator(dgdx_val, nb_iter=96)
train_generator=dgdx
validation_generator=dgdx_val
#pdb.set_trace()
alexnet_model = convnet('alexnet', weights_path="/home/wangnxr/Documents/ecogdeep/convnets-keras/examples/alexnet_weights.h5")
#base_model = VGG16(input_tensor=(Input(shape=(3,224, 224))), include_top=False, weights='imagenet')
base_model = Model(alexnet_model.input, alexnet_model.get_layer("dense_2").output)
frame_a = Input(shape=(3,227,227))
frame_b = Input(shape=(3,227,227))
frame_c = Input(shape=(3,227,227))
frame_d = Input(shape=(3,227,227))
tower1 = base_model(frame_a)
tower2 = base_model(frame_b)
tower3 = base_model(frame_c)
tower4 = base_model(frame_d)
x = merge([tower1, tower2, tower3, tower4], mode='concat')
from keras.optimizers import SGD
from convnetskeras.convnets import preprocess_image_batch, convnet
im = preprocess_image_batch(['examples/dog.jpg'],img_size=(256,256), crop_size=(227,227), color_mode="rgb")
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',weights_path="weights/alexnet_weights.h5", heatmap=False)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
print(out)
rex = np.resize(X_test[i, :, :, :], (227, 227))
X_test_extend[i, 0, :, :] = rex
X_test_extend[i, 1, :, :] = rex
X_test_extend[i, 2, :, :] = rex
X_test = X_test_extend
X_test_test_extend = np.zeros((X_test_test.shape[0], 3, 227, 227))
for i in xrange(X_test_test.shape[0]):
rex = np.resize(X_test_test[i, :, :, :], (227, 227))
X_test_test_extend[i, 0, :, :] = rex
X_test_test_extend[i, 1, :, :] = rex
X_test_test_extend[i, 2, :, :] = rex
X_test_test = X_test_test_extend
if pretrain: # 227*227
alexmodel = convnet('alexnet',
weights_path='alexnet_weights.h5',
heatmap=False,
l1=l1factor,
l2=l2factor)
model = convnet('alexnet',
outdim=2,
l1=l1factor,
l2=l2factor,
usemymil=mymil,
k=mymilk)
for layer, mylayer in zip(alexmodel.layers, model.layers):
print(layer.name)
if mylayer.name == 'mil_1':
break
else:
weightsval = layer.get_weights()
print(len(weightsval))
X_train[i, :, :, :] = x
#X_train = preprocess_image_batch(list_train_images, img_size=(227,227), color_mode='bgr')
#X_train = X_train/255.0
#mean_train = np.mean(X_train)
#std_train = np.std(X_train)
#X_train -= mean_train
#X_train /= std_train
#with open('X_train_stats_alexnet.txt', 'w') as out_file:
# out_file.write(str(mean_train) + "\n")
# out_file.write(str(std_train) + "\n")
base_model = convnet('alexnet',
weights_path="../models/alexnet_weights.h5",
heatmap=False)
# this is the model we will train
model = Sequential()
base_model.layers.pop()
base_model.layers.pop()
model.add(base_model)
#model.add(Flatten(input_shape=base_model.output_shape[1:]))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
print(base_model.summary())
print(model.summary())
# first: train only the top layers (which were randomly initialized)
import os
os.environ['KERAS_BACKEND'] = 'theano'
os.environ['THEANO_FLAGS'] = 'floatX=float32,device=gpu,lib.cnmem=0.8,dnn.conv.algo_bwd_filter=deterministic,dnn.conv.algo_bwd_data=deterministic,blas.ldflags=-LC:/toolkits/openblas-0.2.14-int32/bin -lopenblas'
from keras.optimizers import SGD
from convnetskeras.convnets import preprocess_image_batch, convnet
import numpy as np
DATAPATH = "../USNS/RawData/train/"
imgNames = [DATAPATH+fname for fname in os.listdir(DATAPATH) if "mask" not in fname]
x_train = preprocess_image_batch(imgNames,img_size=(256,256), crop_size=(227,227), color_mode="rgb")
print "Pre-processing done"
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet',weights_path="../USNS/AlexNet/weights/alexnet_weights.h5", heatmap=False)
model.compile(optimizer=sgd, loss='mse')
print "Model Compiled"
train = model.predict(x_train)
print "Predictions made"
np.savez("../USNS/AlexNet/train/Data.npz",X_train=train)
(TOT_0_r[ind_train_0, :, :], TOT_90_r[ind_train_90, :, :],
TOT_180_r[ind_train_180, :, :], TOT_270_r[ind_train_270, :, :]),
axis=0)
newYtest = np.concatenate(
(y[ind_test_0], y[ind_test_90], y[ind_test_180], y[ind_test_270]), axis=0)
newYval = np.concatenate(
(y[ind_val_0], y[ind_val_90], y[ind_val_180], y[ind_val_270]), axis=0)
newYtrain = np.concatenate(
(y[ind_train_0], y[ind_train_90], y[ind_train_180], y[ind_train_270]),
axis=0)
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
###model = convnet('alexnet',weights_path="alexnet_weights.h5", heatmap=False)
###model2 = convnet('alexnet24', weights_path=None, heatmap=False)
model2 = convnet('alexnet24',
weights_path="./tmp/weights_alex_7_5.hdf5",
heatmap=False) ###
model2.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
ny = np.zeros(newYtest.shape[0])
for nf in range(0, newYtest.shape[0]):
for df in range(24):
if newYtest[nf, df] == 1:
ny[nf] = df
score = model2.evaluate(X_test1, newYtest, batch_size=32)
loss_and_metrics = model2.evaluate(X_test1, newYtest)
predict = model2.predict(X_test1, batch_size=32)
from keras.optimizers import SGD
from convnetskeras.convnets import preprocess_image_batch, convnet
from convnetskeras.imagenet_tool import synset_to_dfs_ids
#im = preprocess_image_batch(['examples/bern.jpg'], color_mode="bgr")
im = preprocess_image_batch(['examples/test.png'], color_mode="bgr")
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet(
'alexnet',
weights_path="../../../../models/fully_conv/alexnet_weights.h5",
heatmap=True)
#model = convnet('vgg_16',weights_path="../../../../models/fully_conv/vgg16_weights.h5", heatmap=True)
model.compile(optimizer=sgd, loss='mse')
out = model.predict(im)
s = "n02084071"
ids = synset_to_dfs_ids(s)
heatmap = out[0, ids].sum(axis=0)
# Then, we can get the image
import matplotlib.pyplot as plt
plt.imsave("examples/heatmap.png", heatmap)
X_test = []
X_test_id = []
for fl in files:
flbase = os.path.basename(fl)
im = preprocess_image_batch([fl],
img_size=(256, 256),
crop_size=(227, 227),
color_mode="rgb")
out = model.predict(im)
X_test.append(out.flatten())
X_test_id.append(flbase)
return np.array(X_test), X_test_id
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('alexnet', weights_path="weights/alexnet_weights.h5")
model.layers.pop()
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
model.compile(optimizer=sgd, loss='mse')
p, y, Xid = load_train()
print(p)
train = pd.DataFrame(p, index=Xid)
pt, Xtid = load_test()
test = pd.DataFrame(pt, index=Xtid)
train.to_csv("train.bottleneck.vgg16.csv")
test.to_csv("test.bottleneck.vgg16.csv")
rex = np.resize(X_test[i, :, :, :], (227, 227))
X_test_extend[i, 0, :, :] = rex
X_test_extend[i, 1, :, :] = rex
X_test_extend[i, 2, :, :] = rex
X_test = X_test_extend
X_test_test_extend = np.zeros((X_test_test.shape[0], 3, 227, 227))
for i in xrange(X_test_test.shape[0]):
rex = np.resize(X_test_test[i, :, :, :], (227, 227))
X_test_test_extend[i, 0, :, :] = rex
X_test_test_extend[i, 1, :, :] = rex
X_test_test_extend[i, 2, :, :] = rex
X_test_test = X_test_test_extend
if pretrain: # 227*227
alexmodel = convnet('alexnet',
weights_path='alexnet_weights.h5',
heatmap=False,
l1=l1factor,
l2=l2factor)
model = convnet('alexnet',
outdim=2,
l1=l1factor,
l2=l2factor,
sparsemil=sparsemil,
sparsemill1=sparsemill1,
sparsemill2=sparsemill2)
for layer, mylayer in zip(alexmodel.layers, model.layers):
print(layer.name)
if mylayer.name == 'mil_1':
break
else:
weightsval = layer.get_weights()
X_test = []
X_test_id = []
for fl in files:
flbase = os.path.basename(fl)
im = preprocess_image_batch([fl],
img_size=(256,256),
crop_size=(224,224),
color_mode="bgr")
out = model.predict(im)
X_test.append(out.flatten())
X_test_id.append(flbase)
return np.array(X_test), X_test_id
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model = convnet('vgg_16',weights_path="weights/vgg16_weights.h5")
model.layers.pop()
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
model.compile(optimizer=sgd, loss='mse')
p, y, Xid = load_train()
print(p)
train = pd.DataFrame(p, index=Xid)
pt, Xtid = load_test()
test = pd.DataFrame(pt, index=Xtid)
train.to_csv("train.bottleneck.vgg16.new.csv")
test.to_csv("test.bottleneck.vgg16.new.csv")