My question is simple and simple. What determines the size of the party in training and forecasting the neural network. How to visualize it in order to get a clear idea of how data is transmitted to the network.
Suppose I have autoencoder
encoder = tflearn.input_data(shape=[None, 41])
encoder = tflearn.fully_connected(encoder, 41,activation='relu')
and I take the input as a csv file with 41 functions, so, as I understand it, each of them will be extracted from the csv file and transfer it to 41 neurons of the first layer when the lot size is 1.
But when I increase the batch size to 100, how will 41 functions out of 100 batches in this network be implemented?
model.fit(test_set, test_labels_set, n_epoch=1, validation_set=(valid_set, valid_labels_set),
run_id="auto_encoder", batch_size=100,show_metric=True, snapshot_epoch=False)
Will the party or some operations on them be normalized?
The number of epics is the same for both cases.
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