Pit in LSTM programming by python
No, samples is different from batch_size. samples is the total number of samples you would have. batch_size would be the size of each batch or the number of samples per each batch used in training, like by .fit. 
For example, if samples=128 and batch_size=16, then your data would be divided into 8 batches with each having 16 samples inside during .fit call. 
As another note, time_steps is the total time steps or observations within each sample. It does not make much sense to have it as 1 with LSTM as the main advantage of RNN's in general is to learn the temporal patterns. With time_step=1, there won't be any history to leverage. Here as an example that might help:
Assume that your job is to determine if someone is active or not every hour by looking at they breathing rate and heart rate provided every minute, i.e. 2 features measured at 60 samples per hour. (This is just an example, use accelerometers if you really wanted to do this :)) Let's say you have 128 hours of labeled data. Then your input data would be of shape (128, 60, 2) and your output would be of shape (128, 1).
Here, you have 128 samples, 60 time steps or observations per sample, and two features.
Next you split the data into train, validation, and testing according to the samples. For example, your train, validation, and test data would be of shapes (96, 60, 2), (16, 60, 2), and (16, 60, 2), respectively. 
If you use batch_size=16, your training, validation, and test data would have 6, 1, and 1 batches, respectively.
As we all Know, if we want to train a LSTM network, we must reshape the train dataset by the function numpy.reshape(), and reshaping result is like [samples,time_steps,features]. However, the new shape is influenced by the original one. I have seen some blogs teaching LSTM programming taking 1 as time_steps, and if time_steps is another number, samples will change relevently. My question is that does the samplesequal to batch_size?
X = X.reshape(X.shape[0], 1, X.shape[1])