Fix Python – How to fix RuntimeError “Expected object of scalar type Float but got scalar type Double for argument”?

I’m trying to train a classifier via PyTorch. However, I am experiencing problems with training when I feed the model with training data.
I get this error on y_pred = model(X_trainTensor):

RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #4 ‘mat1’

Here are key parts of my code:
# Hyper-parameters
D_in =….

Fix Python – RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

This:
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
model.to(device)

for data in dataloader:
inputs, labels = data
outputs = model(inputs)

Gives the error:

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

….

Fix Python – What is the use of verbose in Keras while validating the model?

I’m running the LSTM model for the first time.
Here is my model:
opt = Adam(0.002)
inp = Input(…)
print(inp)
x = Embedding(….)(inp)
x = LSTM(…)(x)
x = BatchNormalization()(x)
pred = Dense(5,activation=’softmax’)(x)

model = Model(inp,pred)
model.compile(….)

idx = np.random.permutation(X_train.shape[0])
model.fit(X_train[idx], y_train[idx]….

Fix Python – What does tf.nn.embedding_lookup function do?

tf.nn.embedding_lookup(params, ids, partition_strategy=’mod’, name=None)

I cannot understand the duty of this function. Is it like a lookup table? Which means to return the parameters corresponding to each id (in ids)?
For instance, in the skip-gram model if we use tf.nn.embedding_lookup(embeddings, train_inputs), then for each train_input it fin….

Fix Python – Understanding Keras LSTMs

I am trying to reconcile my understand of LSTMs and pointed out here in this post by Christopher Olah implemented in Keras. I am following the blog written by Jason Brownlee for the Keras tutorial. What I am mainly confused about is,

The reshaping of the data series into [samples, time steps, features] and,
The stateful LSTMs

Lets concentrate on….