def load_dataset():
br = BatchReader2.inputs()
br2 = BatchReader2.inputs(testingData=True)
X, Y = br.getNPArray(3)
X_train = X[:50000 * 0.8].reshape(-1, 1, 48, 48)
y_train = Y[:50000 * 0.8].astype('uint8')
X_val = X[50000 * 0.8:].reshape(-1, 1, 48, 48)
y_val = Y[50000 * 0.8:].astype('uint8')
return X_train, y_train, X_val, y_val
def load_dataset():
br = BatchReader2.inputs()
br2 = BatchReader2.inputs(testingData = True)
X, Y = br.getNPArray(3)
X_train = X[:50000*0.8].reshape(-1, 1, 48, 48)
y_train = Y[:50000*0.8].astype('uint8')
X_val = X[50000*0.8:].reshape(-1, 1, 48, 48)
y_val = Y[50000*0.8:].astype('uint8')
return X_train, y_train, X_val, y_val
def loadData():
sizeTrain = 0.7
sizeVal = 0.2
br = BatchReader2.inputs()
br2 = BatchReader2.inputs(testingData = True)
X, Y = br.getNPArray(2)
n = X.shape[0]
testX = br2.getNPArray(2)
trainX = X[:n*sizeTrain].reshape(-1,1,48,48)
trainY = Y[:n*sizeTrain].astype('uint8')
validationX = X[n*sizeTrain:n*(sizeTrain+sizeVal)+1].reshape(-1,1,48,48)
validationY = Y[n*sizeTrain:n*(sizeTrain+sizeVal)+1].astype('uint8')
print (trainX.shape)
print (validationX.shape)
print (validationY.shape)
tX = X[n*(sizeTrain+sizeVal)+1:].reshape(-1,1,48,48)
tY = Y[n*(sizeTrain+sizeVal)+1:].astype('uint8')
print (tX.shape)
testX = testX.reshape(-1,1,48,48)
return trainX,trainY,validationX,validationY,tX,tY,testX
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer
import BatchReader2
import scipy.misc
import pickle
import CNNL
import CNN
theano.config.floatX = 'float32'
if __name__ == '__main__':
rng = numpy.random.RandomState(42)
br = BatchReader2.inputs()
br2 = BatchReader2.inputs(testingData=True)
X, Y = br.getNPArray(2)
testX = br2.getNPArray(2)
print testX.shape
num_epochs = 200
n = X.shape[0]
sizeTrain = 0.8
trainX = X[:n * sizeTrain].reshape(-1, 1, 48, 48)
trainY = Y[:n * sizeTrain].astype('uint8')
validationX = X[n * sizeTrain:].reshape(-1, 1, 48, 48)
validationY = Y[n * sizeTrain:].astype('uint8')
testX = testX.reshape(-1, 1, 48, 48)
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
network = CNNL.convnetL(input_var)
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer
import BatchReader2
import scipy.misc
import pickle
import CNNL
import CNN
theano.config.floatX = 'float32'
if __name__ == '__main__':
rng = numpy.random.RandomState(42)
br = BatchReader2.inputs()
br2 = BatchReader2.inputs(testingData = True)
X, Y = br.getNPArray(2)
testX = br2.getNPArray(2)
print testX.shape
num_epochs = 200
n = X.shape[0]
sizeTrain = 0.8
trainX = X[:n*sizeTrain].reshape(-1,1,48,48)
trainY = Y[:n*sizeTrain].astype('uint8')
validationX = X[n*sizeTrain:].reshape(-1,1,48,48)
validationY = Y[n*sizeTrain:].astype('uint8')
testX = testX.reshape(-1,1,48,48)
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
network = CNNL.convnetL(input_var)