Load data from csv file with pandas and feed to NN models

[LewsTherin511]

I'm having a problem in loading correctly a .csv file to use as input for a very simple dense NN model.
The csv file contains all the input features and a 'target' column, to use as output for regression.

This is what I'm doing so far:

`
def main():

	batch_size = 500

	## load input file
	df_data = pd.read_csv('some_file.csv', index_col=0)
	## random train/test split
	df_train = df_data.sample(frac=0.8,random_state=200)
	df_test = df_data.drop(df_train.index)

    ## data pre-processing
	df_train.reset_index(drop=True, inplace=True)
	df_test.reset_index(drop=True, inplace=True)	
	y_train = df_train['target'].to_numpy(dtype=np.float64)
	y_test = df_test['target'].to_numpy(dtype=np.float64)
	X_train = df_train.drop(['target'], axis=1).to_numpy(dtype=np.float64)
	X_test = df_test.drop(['target'], axis=1).to_numpy(dtype=np.float64)


	dataset = mx.gluon.data.dataset.ArrayDataset(X_train, y_train)
	data_loader = mx.gluon.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)

	##   building model 
	model = nn.Sequential()
	model.add(nn.Dense(150))
	model.add(nn.Dense(1))
	model.initialize(init.Normal(sigma=0.01))

	## loss function (squared loss)
	loss = gloss.L2Loss()

	## optimization algorithm, specify:
	trainer = gluon.Trainer(model.collect_params(), 'sgd', {'learning_rate': 0.03})

	##   training   #
	num_epochs = 10
	for epoch in range(1, num_epochs + 1):
		for X_batch, Y_batch in data_loader:
			with autograd.record():
				l = loss(model(X_batch), Y_batch)
			l.backward()
			trainer.step(batch_size)
		# overall (entire dataset) loss after epoch
		l = loss(model(X_train), y_train)
		print(f'\nEpoch {epoch}, loss: {l.mean().asnumpy()}')

`

I was getting the error:

mxnet.base.MXNetError: [16:09:03] src/operator/numpy/linalg/./../../tensor/../elemwise_op_common.h:135: Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node at 1-th input: expected float64, got float32

So, I tried converting the data by switching the np.float64 to np.float32, but the I get:

File "/home/lews/anaconda3/envs/gluon/lib/python3.7/site-packages/mxnet/gluon/block.py", line 1136, in forward raise ValueError('In HybridBlock, there must be one NDArray or one Symbol in the input.' ValueError: In HybridBlock, there must be one NDArray or one Symbol in the input. Please check the type of the args.

What is the correct way to load this data?