seq2seq
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farizrahman4u
Tests fail with TypeError on updated keras / theano
Seq2seq installed from github:
pip install --upgrade --no-deps git+https://github.com/farizrahman4u/seq2seq.git
Keras 1.2.0, Theano 0.8.2.
SimpleSeq2Seq test fails, full traceback below (excuse my copy-pasting tests.py)`.
Perhaps related to https://github.com/farizrahman4u/seq2seq/issues/151, just a different backend?
➜ ~ ipython
Python 2.7.13 |Anaconda custom (x86_64)| (default, Dec 20 2016, 23:05:08)
Type "copyright", "credits" or "license" for more information.
IPython 5.1.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: %paste
from seq2seq import SimpleSeq2Seq, Seq2Seq, AttentionSeq2Seq
import numpy as np
from keras.utils.test_utils import keras_test
input_length = 10
input_dim = 2
output_length = 8
output_dim = 3
samples = 100
@keras_test
def test_SimpleSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [SimpleSeq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim))]
models += [SimpleSeq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, nb_epoch=1)
@keras_test
def test_Seq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [Seq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim))]
models += [Seq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), peek=True)]
models += [Seq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), depth=2)]
models += [Seq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), peek=True, depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, nb_epoch=1)
model = Seq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), peek=True, depth=2, teacher_force=True)
model.compile(loss='mse', optimizer='sgd')
model.fit([x, y], y, nb_epoch=1)
@keras_test
def test_AttentionSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [AttentionSeq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim))]
models += [AttentionSeq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), depth=2)]
models += [AttentionSeq2Seq(output_dim=output_dim, output_length=output_length, input_shape=(input_length, input_dim), depth=3)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, nb_epoch=1)
## -- End pasted text --
Using Theano backend.
Using gpu device 0: GeForce GT 750M (CNMeM is disabled, cuDNN 5105)
/Users/olevinkr/anaconda/lib/python2.7/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
In [2]: test_SimpleSeq2Seq()
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/engine/topology.py:376: UserWarning: The `regularizers` property of layers/models is deprecated. Regularization losses are now managed via the `losses` layer/model property.
warnings.warn('The `regularizers` property of layers/models '
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-876b58b53df4> in <module>()
----> 1 test_SimpleSeq2Seq()
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/utils/test_utils.pyc in wrapper(*args, **kwargs)
126 @six.wraps(func)
127 def wrapper(*args, **kwargs):
--> 128 output = func(*args, **kwargs)
129 if K._BACKEND == 'tensorflow':
130 K.clear_session()
<ipython-input-1-983cd492c8bd> in test_SimpleSeq2Seq()
23 for model in models:
24 model.compile(loss='mse', optimizer='sgd')
---> 25 model.fit(x, y, nb_epoch=1)
26
27 @keras_test
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)
662 shuffle=shuffle,
663 class_weight=class_weight,
--> 664 sample_weight=sample_weight)
665
666 def evaluate(self, x, y, batch_size=32, verbose=1,
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch)
1113 else:
1114 ins = x + y + sample_weights
-> 1115 self._make_train_function()
1116 f = self.train_function
1117
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _make_train_function(self)
718 [self.total_loss] + self.metrics_tensors,
719 updates=updates,
--> 720 **self._function_kwargs)
721
722 def _make_test_function(self):
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.pyc in function(inputs, outputs, updates, **kwargs)
927 msg = 'Invalid argument "%s" passed to K.function' % key
928 raise ValueError(msg)
--> 929 return Function(inputs, outputs, updates=updates, **kwargs)
930
931
/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.pyc in __init__(self, inputs, outputs, updates, **kwargs)
906 def __init__(self, inputs, outputs, updates=[], **kwargs):
907 unique_variables_to_update = {}
--> 908 for v, nv in updates:
909 if v not in unique_variables_to_update:
910 unique_variables_to_update[v] = nv
/Users/olevinkr/anaconda/lib/python2.7/site-packages/theano/tensor/var.pyc in __iter__(self)
551 except TypeError:
552 # This prevents accidental iteration via builtin.sum(self)
--> 553 raise TypeError(('TensorType does not support iteration. '
554 'Maybe you are using builtin.sum instead of '
555 'theano.tensor.sum? (Maybe .max?)'))
TypeError: TensorType does not support iteration. Maybe you are using builtin.sum instead of theano.tensor.sum? (Maybe .max?)
