Pit in LSTM programming by python
No. Samples are not equal to batch size. Samples means the number of rows in your data-set. Your training data-set is divided into number of batches and pass it to the network to train. 
In simple words,
Imagine your data-set has 30 samples, and you define your batch_size as 3. 
That means the 30 samples divided into 10 batches(30 divided by you defined batch_size = 10). When you train you model, at a time only 3 rows of data will be be pushed to the neural network and then next 3 rows will be push to the neural network. Like wise whole data-set will push to the neural network.
Samples/Batch_size = Number of batches
Remember that batch_size and number of batches are two different things.
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])