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- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- import numpy as np
- from six.moves import xrange
-
- __all__ = [
- 'minibatches',
- 'seq_minibatches',
- 'seq_minibatches2',
- 'ptb_iterator',
- ]
-
-
- def minibatches(inputs=None, targets=None, batch_size=None, allow_dynamic_batch_size=False, shuffle=False):
- """Generate a generator that input a group of example in numpy.array and
- their labels, return the examples and labels by the given batch size.
-
- Parameters
- ----------
- inputs : numpy.array
- The input features, every row is a example.
- targets : numpy.array
- The labels of inputs, every row is a example.
- batch_size : int
- The batch size.
- allow_dynamic_batch_size: boolean
- Allow the use of the last data batch in case the number of examples is not a multiple of batch_size, this may result in unexpected behaviour if other functions expect a fixed-sized batch-size.
- shuffle : boolean
- Indicating whether to use a shuffling queue, shuffle the dataset before return.
-
- Examples
- --------
- >>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
- >>> y = np.asarray([0,1,2,3,4,5])
- >>> for batch in tl.iterate.minibatches(inputs=X, targets=y, batch_size=2, shuffle=False):
- >>> print(batch)
- ... (array([['a', 'a'], ['b', 'b']], dtype='<U1'), array([0, 1]))
- ... (array([['c', 'c'], ['d', 'd']], dtype='<U1'), array([2, 3]))
- ... (array([['e', 'e'], ['f', 'f']], dtype='<U1'), array([4, 5]))
-
- Notes
- -----
- If you have two inputs and one label and want to shuffle them together, e.g. X1 (1000, 100), X2 (1000, 80) and Y (1000, 1), you can stack them together (`np.hstack((X1, X2))`)
- into (1000, 180) and feed to ``inputs``. After getting a batch, you can split it back into X1 and X2.
-
- """
- if len(inputs) != len(targets):
- raise AssertionError("The length of inputs and targets should be equal")
-
- if shuffle:
- indices = np.arange(len(inputs))
- np.random.shuffle(indices)
-
- # for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
- # chulei: handling the case where the number of samples is not a multiple of batch_size, avoiding wasting samples
- for start_idx in range(0, len(inputs), batch_size):
- end_idx = start_idx + batch_size
- if end_idx > len(inputs):
- if allow_dynamic_batch_size:
- end_idx = len(inputs)
- else:
- break
- if shuffle:
- excerpt = indices[start_idx:end_idx]
- else:
- excerpt = slice(start_idx, end_idx)
- if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle ==True):
- # zsdonghao: for list indexing when shuffle==True
- yield [inputs[i] for i in excerpt], [targets[i] for i in excerpt]
- else:
- yield inputs[excerpt], targets[excerpt]
-
-
- def seq_minibatches(inputs, targets, batch_size, seq_length, stride=1):
- """Generate a generator that return a batch of sequence inputs and targets.
- If `batch_size=100` and `seq_length=5`, one return will have 500 rows (examples).
-
- Parameters
- ----------
- inputs : numpy.array
- The input features, every row is a example.
- targets : numpy.array
- The labels of inputs, every element is a example.
- batch_size : int
- The batch size.
- seq_length : int
- The sequence length.
- stride : int
- The stride step, default is 1.
-
- Examples
- --------
- Synced sequence input and output.
-
- >>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
- >>> y = np.asarray([0, 1, 2, 3, 4, 5])
- >>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=y, batch_size=2, seq_length=2, stride=1):
- >>> print(batch)
- ... (array([['a', 'a'], ['b', 'b'], ['b', 'b'], ['c', 'c']], dtype='<U1'), array([0, 1, 1, 2]))
- ... (array([['c', 'c'], ['d', 'd'], ['d', 'd'], ['e', 'e']], dtype='<U1'), array([2, 3, 3, 4]))
-
- Many to One
-
- >>> return_last = True
- >>> num_steps = 2
- >>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
- >>> Y = np.asarray([0,1,2,3,4,5])
- >>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=Y, batch_size=2, seq_length=num_steps, stride=1):
- >>> x, y = batch
- >>> if return_last:
- >>> tmp_y = y.reshape((-1, num_steps) + y.shape[1:])
- >>> y = tmp_y[:, -1]
- >>> print(x, y)
- ... [['a' 'a']
- ... ['b' 'b']
- ... ['b' 'b']
- ... ['c' 'c']] [1 2]
- ... [['c' 'c']
- ... ['d' 'd']
- ... ['d' 'd']
- ... ['e' 'e']] [3 4]
-
- """
- if len(inputs) != len(targets):
- raise AssertionError("The length of inputs and targets should be equal")
-
- n_loads = (batch_size * stride) + (seq_length - stride)
-
- for start_idx in range(0, len(inputs) - n_loads + 1, (batch_size * stride)):
- seq_inputs = np.zeros((batch_size, seq_length) + inputs.shape[1:], dtype=inputs.dtype)
- seq_targets = np.zeros((batch_size, seq_length) + targets.shape[1:], dtype=targets.dtype)
- for b_idx in xrange(batch_size):
- start_seq_idx = start_idx + (b_idx * stride)
- end_seq_idx = start_seq_idx + seq_length
- seq_inputs[b_idx] = inputs[start_seq_idx:end_seq_idx]
- seq_targets[b_idx] = targets[start_seq_idx:end_seq_idx]
- flatten_inputs = seq_inputs.reshape((-1, ) + inputs.shape[1:])
- flatten_targets = seq_targets.reshape((-1, ) + targets.shape[1:])
- yield flatten_inputs, flatten_targets
-
-
- def seq_minibatches2(inputs, targets, batch_size, num_steps):
- """Generate a generator that iterates on two list of words. Yields (Returns) the source contexts and
- the target context by the given batch_size and num_steps (sequence_length).
- In TensorFlow's tutorial, this generates the `batch_size` pointers into the raw PTB data, and allows minibatch iteration along these pointers.
-
- Parameters
- ----------
- inputs : list of data
- The context in list format; note that context usually be represented by splitting by space, and then convert to unique word IDs.
- targets : list of data
- The context in list format; note that context usually be represented by splitting by space, and then convert to unique word IDs.
- batch_size : int
- The batch size.
- num_steps : int
- The number of unrolls. i.e. sequence length
-
- Yields
- ------
- Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
-
- Raises
- ------
- ValueError : if batch_size or num_steps are too high.
-
- Examples
- --------
- >>> X = [i for i in range(20)]
- >>> Y = [i for i in range(20,40)]
- >>> for batch in tl.iterate.seq_minibatches2(X, Y, batch_size=2, num_steps=3):
- ... x, y = batch
- ... print(x, y)
- ...
- ... [[ 0. 1. 2.]
- ... [ 10. 11. 12.]]
- ... [[ 20. 21. 22.]
- ... [ 30. 31. 32.]]
- ...
- ... [[ 3. 4. 5.]
- ... [ 13. 14. 15.]]
- ... [[ 23. 24. 25.]
- ... [ 33. 34. 35.]]
- ...
- ... [[ 6. 7. 8.]
- ... [ 16. 17. 18.]]
- ... [[ 26. 27. 28.]
- ... [ 36. 37. 38.]]
-
- Notes
- -----
- - Hint, if the input data are images, you can modify the source code `data = np.zeros([batch_size, batch_len)` to `data = np.zeros([batch_size, batch_len, inputs.shape[1], inputs.shape[2], inputs.shape[3]])`.
- """
- if len(inputs) != len(targets):
- raise AssertionError("The length of inputs and targets should be equal")
-
- data_len = len(inputs)
- batch_len = data_len // batch_size
- # data = np.zeros([batch_size, batch_len])
- data = np.zeros((batch_size, batch_len) + inputs.shape[1:], dtype=inputs.dtype)
- data2 = np.zeros([batch_size, batch_len])
-
- for i in range(batch_size):
- data[i] = inputs[batch_len * i:batch_len * (i + 1)]
- data2[i] = targets[batch_len * i:batch_len * (i + 1)]
-
- epoch_size = (batch_len - 1) // num_steps
-
- if epoch_size == 0:
- raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
-
- for i in range(epoch_size):
- x = data[:, i * num_steps:(i + 1) * num_steps]
- x2 = data2[:, i * num_steps:(i + 1) * num_steps]
- yield (x, x2)
-
-
- def ptb_iterator(raw_data, batch_size, num_steps):
- """Generate a generator that iterates on a list of words, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm.py>`__.
- Yields the source contexts and the target context by the given batch_size and num_steps (sequence_length).
-
- In TensorFlow's tutorial, this generates `batch_size` pointers into the raw
- PTB data, and allows minibatch iteration along these pointers.
-
- Parameters
- ----------
- raw_data : a list
- the context in list format; note that context usually be
- represented by splitting by space, and then convert to unique
- word IDs.
- batch_size : int
- the batch size.
- num_steps : int
- the number of unrolls. i.e. sequence_length
-
- Yields
- ------
- Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
- The second element of the tuple is the same data time-shifted to the
- right by one.
-
- Raises
- ------
- ValueError : if batch_size or num_steps are too high.
-
- Examples
- --------
- >>> train_data = [i for i in range(20)]
- >>> for batch in tl.iterate.ptb_iterator(train_data, batch_size=2, num_steps=3):
- >>> x, y = batch
- >>> print(x, y)
- ... [[ 0 1 2] <---x 1st subset/ iteration
- ... [10 11 12]]
- ... [[ 1 2 3] <---y
- ... [11 12 13]]
- ...
- ... [[ 3 4 5] <--- 1st batch input 2nd subset/ iteration
- ... [13 14 15]] <--- 2nd batch input
- ... [[ 4 5 6] <--- 1st batch target
- ... [14 15 16]] <--- 2nd batch target
- ...
- ... [[ 6 7 8] 3rd subset/ iteration
- ... [16 17 18]]
- ... [[ 7 8 9]
- ... [17 18 19]]
- """
- raw_data = np.array(raw_data, dtype=np.int32)
-
- data_len = len(raw_data)
- batch_len = data_len // batch_size
- data = np.zeros([batch_size, batch_len], dtype=np.int32)
- for i in range(batch_size):
- data[i] = raw_data[batch_len * i:batch_len * (i + 1)]
-
- epoch_size = (batch_len - 1) // num_steps
-
- if epoch_size == 0:
- raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
-
- for i in range(epoch_size):
- x = data[:, i * num_steps:(i + 1) * num_steps]
- y = data[:, i * num_steps + 1:(i + 1) * num_steps + 1]
- yield (x, y)
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