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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, 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.
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.
"""
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
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]
"""
assert len(inputs) == len(targets)
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]])`.
"""
assert len(inputs) == len(targets)
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_state_is_tuple.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)