def main(ftrain, fdev=None, fmodel='model/model.pickle.gz'):
# Load data
print 'Loading training data ...'
data = load(gzip.open(ftrain))
M, labels = data['data'], data['labels']
# Load dev data
if fdev is not None:
print 'Loading dev data ...'
devdata = load(gzip.open(fdev))
devM, devlabels = devdata['data'], devdata['labels']
else:
devM, devlabels = None, None
# Training with specified parameters
print 'Training ...'
clf = Classifier()
clf.train(M, labels, devM, devlabels)
clf.savemodel(fmodel)
def main(ftrain, fdev=None, fmodel='model/model.pickle.gz'):
# Load data
print 'Loading training data ...'
data = load(gzip.open(ftrain))
M, labels = data['data'], data['labels']
# Load dev data
if fdev is not None:
print 'Loading dev data ...'
devdata = load(gzip.open(fdev))
devM, devlabels = devdata['data'], devdata['labels']
else:
devM, devlabels = None, None
# Training with specified parameters
print 'Training ...'
clf = Classifier()
clf.train(M, labels, devM, devlabels)
clf.savemodel(fmodel)
from model.nets import *
from model.train_functions import *
from model.classifier import Classifier
from scipy import misc
import numpy as np
import pandas as pd
params = read_params('Project_path/params.txt')
data = Cifar10_Data()
data.load_and_split(params['input_data_path'], params['labels_path'])
#xtrain=data.train_idxs
#xval=data.val_idxs
#batch = data.get_train_feed_dict('X','y','train',128)
#%%
cls = Classifier(params, data.Ndims, net=convnet2)
cls.train(data, epochs=10, batch_size=128)
#cls.load_weights_from_checkpoint(params['pre-traind_model_path'])
#%% Get The Test Data And Classify It
test_path = params['test_data_path']
labels = []
for batch_num in range(600):
X = []
for idx in range(500):
img_path = test_path + str(batch_num * 500 + idx + 1) + '.png'
X.append(misc.imread(img_path))
X = np.array(X)
X = (X - data.mean) / (data.std + 1e-7)
preds = cls.predict(X)
preds = np.argmax(preds, axis=1)
for i in np.arange(preds.shape[0]):
