from data_load_utils import *
from data_loader import *
from NN.CNN import *
loader = data_loader('train_samples.pkl', from_chunk=False)
X, Y = loader.load_samples(num_elements=8)
model = CNN(batch_size=8)
for i in range(1000):
loss, acc = model.train_on_batch(X, Y, 0.01)
if i + 1 % 10:
print "epoch %d, accuracy %.4f, loss %.4f" % (i + 1, acc, loss)
# model.save_weights('model_weights_cnn_'+str(epoch_done+i+1)+'_'+str(curr_avg_acc)+'.h5',overwrite=True)
return model
if __name__ == '__main__':
assert (len(argv) >= 2)
print "reading arguments"
train_samples_file = argv[1]
test_samples_file = argv[2]
print "Initializing data loading"
#loader_train1 = data_loader(train_samples_file, from_chunk = False)
#loader_train2 = data_loader(train_samples_file, from_chunk = False)
loader_train = data_loader_new(train_samples_file)
test_loader = data_loader_new(test_samples_file)
print "Creating CNN architecture....."
model = CNN(batch_size=8)
# model_arch_json = model.to_json()
# pickle.dump(model_arch_json,open('model_cnn_more_droput.json.pkl','wb'))
print "CNN architechture created"
print "Starting Training..."
#num_evaluate = 10
#for i in range(num_evaluate):
# model = train_model_with_parallel_loading(model,loader,num_epoch=2)
# write_to_file("Evaluating model performance\n")
# model = evaluate_model_with_parallel_loading(model,test_loader,num_epoch=1)
#model = train_model(model,loader)
model = train_model_with_parallel_loading(model,
loader_train,
num_epoch=300,
test_loader=test_loader,
# if os.fork() == 0:
# while True:
# if q_test.qsize() < 5:
# Xt , Yt = test_loader.load_samples(num_elements=250)
# #print "Loaded batch"
# q_test.put([Xt,Yt])
#X,Y = loader_train.load_samples(num_elements = samples_train_count)
X_test, Y_test = test_loader.load_samples(num_elements=samples_test_count,
transform=False)
#datagen = ImageDataGenerator(featurewise_std_normalization=True,featurewise_center=True,rotation_range=20,width_shift_range=0.2,height_shift_range=0.2, horizontal_flip=True)
#datagen.fit(X)
#test_data_gen = ImageDataGenerator(featurewise_std_normalization=True,featurewise_center=True)
#test_data_gen.fit(X)
print "Creating CNN architecture....."
model = CNN(batch_size=32)
# model_arch_json = model.to_json()
# pickle.dump(model_arch_json,open('model_cnn_more_droput.json.pkl','wb'))
print "CNN architechture created"
print "Starting Training..."
#num_evaluate = 10
#for i in range(num_evaluate):
# model = train_model_with_parallel_loading(model,loader,num_epoch=2)
# write_to_file("Evaluating model performance\n")
# model = evaluate_model_with_parallel_loading(model,test_loader,num_epoch=1)
#model = train_model(model,loader)
model = train_model_with_parallel_loading(
model,
q_train,
X_test,
from data_load_utils import *
from data_loader import *
from NN.CNN import *
loader = data_loader('train_samples.pkl',from_chunk=False)
X,Y = loader.load_samples(num_elements=8)
model = CNN(batch_size = 8)
for i in range(1000):
loss, acc = model.train_on_batch(X,Y,0.01)
if i+1 % 10:
print "epoch %d, accuracy %.4f, loss %.4f"%(i+1,acc,loss)
from NN.CNN import * from sys import argv network = CNN(batch_size=5) network.load_model_params_dumb(argv[1]) from pre_trained_embedd import * embedder = image_embedder(img_files_list=argv[2::], network=network) #hello world
# if os.fork() == 0:
# while True:
# if q_test.qsize() < 5:
# Xt , Yt = test_loader.load_samples(num_elements=250)
# #print "Loaded batch"
# q_test.put([Xt,Yt])
#X,Y = loader_train.load_samples(num_elements = samples_train_count)
X_test, Y_test = test_loader.load_samples(num_elements=samples_test_count,transform=False)
test_loader = None
#datagen = ImageDataGenerator(featurewise_std_normalization=True,featurewise_center=True,rotation_range=20,width_shift_range=0.2,height_shift_range=0.2, horizontal_flip=True)
#datagen.fit(X)
#test_data_gen = ImageDataGenerator(featurewise_std_normalization=True,featurewise_center=True)
#test_data_gen.fit(X)
print "Creating CNN architecture....."
model = CNN(batch_size=16)
if len(argv) >= 4:
model.load_model_params_dumb(argv[3])
# model_arch_json = model.to_json()
# pickle.dump(model_arch_json,open('model_cnn_more_droput.json.pkl','wb'))
print "CNN architechture created"
print "Starting Training..."
#num_evaluate = 10
#for i in range(num_evaluate):
# model = train_model_with_parallel_loading(model,loader,num_epoch=2)
# write_to_file("Evaluating model performance\n")
# model = evaluate_model_with_parallel_loading(model,test_loader,num_epoch=1)
#model = train_model(model,loader)
model = train_model_with_parallel_loading(model,q_train, X_test, Y_test, batch_size = 16, num_epoch=1000, samples_train_count=samples_train_count, samples_test_count=samples_test_count, datagen=None, test_data_gen=None)
