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, )