def load_data():
data = _load_data()
X_train, y_train = data[0]
X_valid, y_valid = data[1]
X_test, y_test = data[2]
# reshape for convolutions
X_train = X_train.reshape((X_train.shape[0], 1, 28, 28))
X_valid = X_valid.reshape((X_valid.shape[0], 1, 28, 28))
X_test = X_test.reshape((X_test.shape[0], 1, 28, 28))
return dict(
X_train=theano.shared(lasagne.utils.floatX(X_train)),
y_train=T.cast(theano.shared(y_train), "int32"),
X_valid=theano.shared(lasagne.utils.floatX(X_valid)),
y_valid=T.cast(theano.shared(y_valid), "int32"),
X_test=theano.shared(lasagne.utils.floatX(X_test)),
y_test=T.cast(theano.shared(y_test), "int32"),
num_examples_train=X_train.shape[0],
num_examples_valid=X_valid.shape[0],
num_examples_test=X_test.shape[0],
input_height=X_train.shape[2],
input_width=X_train.shape[3],
output_dim=10,
)
def load_data():
data = _load_data()
# #KAGGLE MNIST:
# train_df = pd.read_csv('./data/train.csv')
# test_df = pd.read_csv('./data/test.csv')
train_label = train_df.values[:, 0]
train_data = train_df.values[:, 1:]
X_train, y_train = data[0]
## X_train, y_train = train_df.values[:, 1:], train_df.values[:, 0]
X_valid, y_valid = data[1]
X_test, y_test = data[2]
# reshape for convolutions
X_train = X_train.reshape((X_train.shape[0], 1, 28, 28))
X_valid = X_valid.reshape((X_valid.shape[0], 1, 28, 28))
X_test = X_test.reshape((X_test.shape[0], 1, 28, 28))
return dict(
X_train=theano.shared(lasagne.utils.floatX(X_train)),
y_train=T.cast(theano.shared(y_train), 'int32'),
X_valid=theano.shared(lasagne.utils.floatX(X_valid)),
y_valid=T.cast(theano.shared(y_valid), 'int32'),
X_test=theano.shared(lasagne.utils.floatX(X_test)),
y_test=T.cast(theano.shared(y_test), 'int32'),
num_examples_train=X_train.shape[0],
num_examples_valid=X_valid.shape[0],
num_examples_test=X_test.shape[0],
input_width=X_train.shape[2],
input_height=X_train.shape[3],
output_dim=10,
)
def load_data():
data = _load_data()
X_train, y_train = data[0]
X_valid, y_valid = data[1]
X_test, y_test = data[2]
# reshape for convolutions
X_train = X_train.reshape((X_train.shape[0], 1, 28, 28))
X_valid = X_valid.reshape((X_valid.shape[0], 1, 28, 28))
X_test = X_test.reshape((X_test.shape[0], 1, 28, 28))
return dict(
X_train=theano.shared(lasagne.utils.floatX(X_train)),
y_train=T.cast(theano.shared(y_train), 'int32'),
X_valid=theano.shared(lasagne.utils.floatX(X_valid)),
y_valid=T.cast(theano.shared(y_valid), 'int32'),
X_test=theano.shared(lasagne.utils.floatX(X_test)),
y_test=T.cast(theano.shared(y_test), 'int32'),
num_examples_train=X_train.shape[0],
num_examples_valid=X_valid.shape[0],
num_examples_test=X_test.shape[0],
input_height=X_train.shape[2],
input_width=X_train.shape[3],
output_dim=10,
)
def load_data():
data = _load_data()
# #KAGGLE MNIST:
# train_df = pd.read_csv('./data/train.csv')
# test_df = pd.read_csv('./data/test.csv')
train_label = train_df.values[:, 0]
train_data = train_df.values[:, 1:]
X_train, y_train = data[0]
## X_train, y_train = train_df.values[:, 1:], train_df.values[:, 0]
X_valid, y_valid = data[1]
X_test, y_test = data[2]
# reshape for convolutions
X_train = X_train.reshape((X_train.shape[0], 1, 28, 28))
X_valid = X_valid.reshape((X_valid.shape[0], 1, 28, 28))
X_test = X_test.reshape((X_test.shape[0], 1, 28, 28))
return dict(
X_train=theano.shared(lasagne.utils.floatX(X_train)),
y_train=T.cast(theano.shared(y_train), 'int32'),
X_valid=theano.shared(lasagne.utils.floatX(X_valid)),
y_valid=T.cast(theano.shared(y_valid), 'int32'),
X_test=theano.shared(lasagne.utils.floatX(X_test)),
y_test=T.cast(theano.shared(y_test), 'int32'),
num_examples_train=X_train.shape[0],
num_examples_valid=X_valid.shape[0],
num_examples_test=X_test.shape[0],
input_width=X_train.shape[2],
input_height=X_train.shape[3],
output_dim=10,
)
