Reason of the question"StopIteration"

[GrayPaul]

In the founction "generate_train_batch" belong to dara_processor.py. i = 0 while i < (self.len_train - seq_len): x_batch = [] y_batch = [] for b in range(batch_size): if i >= (self.len_train - seq_len):

stop-condition for a smaller final batch if data doesn't divide evenly

                yield np.array(x_batch), np.array(y_batch)
                i = 0
            x, y = self._next_window(i, seq_len, normalise)
            x_batch.append(x)
            y_batch.append(y)
            i += 1
        yield np.array(x_batch), np.array(y_batch)

sometimes, i==self.len_train-seq_len. So it cannot satisfy the while loop .

[lizhogn]

Is this the intent of the author or a bug in the program

[lizhogn]

If I want to iterate more times to better train the model, how can I improve the problem

[shenjian5]

Maybe this is a small mistake. Perhaps you can refer to the following modification code:

def generate_train_batch(self, seq_len, batch_size, normalise):
    '''Yield a generator of training data from filename on given list of cols split for train/test'''
    i = 0
    x_batch = []
    y_batch = []
    # while i < (self.len_train - seq_len):
    while True:
        for b in range(batch_size):
            x, y = self._next_window(i, seq_len, normalise)
            x_batch.append(x)
            y_batch.append(y)
            i += 1
            if i == (self.len_train - seq_len):
                # stop-condition for a smaller final batch if data doesn't divide evenly
                i = 0
                yield np.array(x_batch), np.array(y_batch)
                x_batch = []
                y_batch = []

[lizhogn]

OK， thanks very much👍

在 2020年5月3日，下午9:22，shenjian5 notifications@github.com 写道：

 Maybe this is a small mistake. Perhaps you can refer to the following modification code:

def generate_train_batch(self, seq_len, batch_size, normalise): '''Yield a generator of training data from filename on given list of cols split for train/test''' i = 0 x_batch = [] y_batch = []

while i < (self.len_train - seq_len):

while True:
    for b in range(batch_size):
        x, y = self._next_window(i, seq_len, normalise)
        x_batch.append(x)
        y_batch.append(y)
        i += 1
        if i == (self.len_train - seq_len):
            # stop-condition for a smaller final batch if data doesn't divide evenly
            i = 0
            yield np.array(x_batch), np.array(y_batch)
            x_batch = []
            y_batch = []

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[lizhogn]

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[Catheriana]

@shenjian5 It works for me.Thanks~!