How to shape 2-feature input data for LSTM - arrays

Question

I am using a RNN with LSTM nodes in Keras for a time series prediction. I have two input features and one output feature and I'm using a sliding window of size 4 and stepsize 1.
So I'm trying to prepare the arrays accordingly for the LSTM to handle the data. However, the dimensions don't seem to be right. I've got it to a point where I got the 3D array in the right shape for the network to take it, but the way the data is set up in the array does not seem right to me.
So, looking only at the training data, this is the raw data from the file:
train_input = df[['a','b']].values (this is of shape (354, 2))
train_output = df[['c']].values (this is of shape (354, 1))
Next I scale the data, after which the shape still remains the same. And then I want to use a loop in order to bring the data into the sliding window shape (window size 4, range 354):
train_input_window = []
train_output_window = []
for i in range(4, 354):
train_input_window.append(train_input_scaled[i-4:i, 0])
train_input_window.append(train_input_scaled[i-4:i, 1])
train_output_window.append(train_output_scaled[i, 0])
train_input_window = np.array(train_input_window)
train_output_window = np.array(train_output_window)
Now train_input_window is of shape (700, 4)
and train_output_window is of shape (350,)
So this is where the problem lies, I think. Because I can reshape the data into a 3D array that will work:
train_input3D = np.reshape(train_input_window, (350,4,2))
train_output3D = np.reshape(train_output_window, (350,1,1))
but I just don't think that the data is arranged correctly inside the arrays.
the training input looks somethin like this:
print(train_input3D)
[[[a a]
[a a]
[b b]
[b b]]
[[a a]
[a a]
[b b]
[b b]].....
shouldn't it be
[[[a b]
[a b]
[a b]
[a b]]
[[a b]
[a b]
[a b]
[a b]].....
I tried so much different stuff, and by now I'm so confused that I just hope I'm not also confusing everyone here by trying to explain.
So, is the input shape that I think my array should be in correct for what Im trying? If so, how do I arrange it that way?
Here is my complete code:
#Read Data
df = pd.ExcelFile('GPT.xlsx').parse('7Avg')
# Training Data
train_input = df[['Precip_7Sum','Temp_7Avg']].values#
train_output = df[['GWL_7Avg']].values
# Testing Data
test_input = df[['Precip_7SumT','Temp_7AvgT']].values#
test_output = df[['GWL_7AvgT']].values
# normalize / scale Data
input_scaler = MinMaxScaler(feature_range = (0, 1))
output_scaler = MinMaxScaler(feature_range = (0, 1))
train_input_scaled = input_scaler.fit_transform(train_input)
train_output_scaled = output_scaler.fit_transform(train_output)
test_input_scaled = input_scaler.transform(test_input)
test_output_scaled = output_scaler.transform(test_output)
# Convert Data into sliding window format
train_input_window = []
train_output_window = []
for i in range(4, 354):
train_input_window.append(train_input_scaled[i-4:i, 0])
train_input_window.append(train_input_scaled[i-4:i, 1])
train_output_window.append(train_output_scaled[i, 0])
train_input_window = np.array(train_input_window)
train_output_window = np.array(train_output_window)
test_input_window = []
test_output_window = []
for i in range(4, 354):
test_input_window.append(train_input_scaled[i-4:i, 0])
test_input_window.append(train_input_scaled[i-4:i, 1])
test_output_window.append(train_output_scaled[i, 0])
test_input_window = np.array(test_input_window)
test_output_window = np.array(test_output_window)
# Convert data into 3-D Formats
train_input3D = np.reshape(train_input_window, (350,train_input_window.shape[1],2)) # 3D tensor with shape (batch_size, timesteps, input_dim) // (nr. of samples, nr. of timesteps, nr. of features)
train_output3D = np.reshape(train_output_window, (350,1,1)) #
test_input3D = np.reshape(test_input_window, (350,test_input_window.shape[1],2))
# Instantiate model class
model = Sequential()
# Add LSTM layer
model.add(LSTM(units=1, return_sequences = True, input_shape = (4,2)))
# Add dropout layer to avoid over-fitting
model.add(Dropout(0.2))
# add three more LSTM and Dropouts
model.add(LSTM(units=1, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=1, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=1, return_sequences=True))
model.add(Dropout(0.2))
# Create dense layer at the end of the model to make model more robust
model.add(Dense(units = 1, output_shape = (4,1)))
# Compile model
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
# Training
model.fit(train_input3D, train_output_window, epochs = 100, batch_size = 4)
# Testing / predictions
train_predictions = model.predict(train_input3D)
test_predictions = model.predict(test_input3D)
# Reverse scaling of data for output data
train_predictions = input_scaler.inverse_transform(train_predictions)
test_predictions = input_scaler.inverse_transform(test_predictions)
orig_data = train_output.append(test_output)
Every help on this would be much appreciated. I hope I could get my problem across clearly enough and that someone who could help actually reads all of this :D