jonkrohn
commented
5 years ago Hi @tromgy -- I like this idea, however I'm unable to replicate the error. Are you using the Dockerfile dependencies provided here including Keras 2.2.0?
Closed tromgy closed 5 years ago
jonkrohn
commented
5 years ago Hi @tromgy -- I like this idea, however I'm unable to replicate the error. Are you using the Dockerfile dependencies provided here including Keras 2.2.0?
tromgy
commented
5 years ago Good point, I have compared and my docker file is identical (I have recently re-cloned from this new repo and rebuilt the docker image).
However when I did
print(keras.__version__)right in that same notebook I got this 2.0.8
The Docker file says 2.2.0 and the base image doesn't list Keras, so I'm at a loss where that version mismatch might be coming from.
tromgy
commented
5 years ago OK, so it might have been running an old docker image somehow, as I have restarted everything and made sure it got docker from the latest directory, and lo and behold, keras.__version__ reports 2.2.0
But, I still see the same error from Keras if I use standard division n_dense/4
Here's the complete error stack:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-eaf9fd2c6473> in <module>()
30
31 dense_2 = Dense(n_dense/4,
---> 32 activation='relu', name='dense_2')(drop_dense_layer)
33 dropout_2 = Dropout(dropout, name='drop_dense_2')(dense_2)
34
/opt/conda/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
430 '`layer.build(batch_input_shape)`')
431 if len(input_shapes) == 1:
--> 432 self.build(input_shapes[0])
433 else:
434 self.build(input_shapes)
/opt/conda/lib/python3.6/site-packages/keras/layers/core.py in build(self, input_shape)
870 name='kernel',
871 regularizer=self.kernel_regularizer,
--> 872 constraint=self.kernel_constraint)
873 if self.use_bias:
874 self.bias = self.add_weight(shape=(self.units,),
/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/opt/conda/lib/python3.6/site-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
247 if dtype is None:
248 dtype = K.floatx()
--> 249 weight = K.variable(initializer(shape),
250 dtype=dtype,
251 name=name,
/opt/conda/lib/python3.6/site-packages/keras/initializers.py in __call__(self, shape, dtype)
216 limit = np.sqrt(3. * scale)
217 return K.random_uniform(shape, -limit, limit,
--> 218 dtype=dtype, seed=self.seed)
219
220 def get_config(self):
/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in random_uniform(shape, minval, maxval, dtype, seed)
4075 seed = np.random.randint(10e6)
4076 return tf.random_uniform(shape, minval=minval, maxval=maxval,
-> 4077 dtype=dtype, seed=seed)
4078
4079
/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py in random_uniform(shape, minval, maxval, dtype, seed, name)
240 shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
241 else:
--> 242 rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2)
243 return math_ops.add(rnd * (maxval - minval), minval, name=name)
244
/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_random_ops.py in random_uniform(shape, dtype, seed, seed2, name)
672 _, _, _op = _op_def_lib._apply_op_helper(
673 "RandomUniform", shape=shape, dtype=dtype, seed=seed, seed2=seed2,
--> 674 name=name)
675 _result = _op.outputs[:]
676 _inputs_flat = _op.inputs
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
607 _SatisfiesTypeConstraint(base_type,
608 _Attr(op_def, input_arg.type_attr),
--> 609 param_name=input_name)
610 attrs[input_arg.type_attr] = attr_value
611 inferred_from[input_arg.type_attr] = input_name
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _SatisfiesTypeConstraint(dtype, attr_def, param_name)
58 "allowed values: %s" %
59 (param_name, dtypes.as_dtype(dtype).name,
---> 60 ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
61
62
TypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64Now looking at it carefully it seems that the error originates in Tensorflow. I checked that version (the notebooks don't directly import Tensorflow) and it shows 1.8.0 just as listed in the docker file
jonkrohn
commented
5 years ago Huh, that's weird. Thank you very much for bringing this to my attention, @tromgy -- I can't take the time to resolve this today, but will ASAP.
jonkrohn
commented
5 years ago I've figured out the discrepancy between our docker images @tromgy ... jupyter/scipy-notebook:e7000ca1416d specified in the Dockerfile corresponds to Python 3.6.5. Any insight into how you ended up with 3.6.7?
tromgy
commented
5 years ago It might have been that "old" docker image, although I don't know where it could have came from. Right now I'm sure running the latest Docker image that I built from the docker file in this repo on May 20.
I added this code to the notebook:
# report versions
import sys
print('Python:', sys.version)
import tensorflow
print('Tensorflow:', tensorflow.__version__)
print('Keras:', keras.__version__)
dense_2 = Dense(n_dense/4,
activation='relu', name='dense_2')(drop_dense_layer)
dropout_2 = Dropout(dropout, name='drop_dense_2')(dense_2)
# sigmoid output layer:
predictions = Dense(1, activation='sigmoid', name='output')(dropout_2)
# create model:
model = Model(input_layer, predictions)and that's what I get when I run this cell:
Python: 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:39:56)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
Tensorflow: 1.8.0
Keras: 2.2.0
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-3ff1d14e3d09> in <module>()
40
41 dense_2 = Dense(n_dense/4,
---> 42 activation='relu', name='dense_2')(drop_dense_layer)
43 dropout_2 = Dropout(dropout, name='drop_dense_2')(dense_2)
44
/opt/conda/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
430 '`layer.build(batch_input_shape)`')
431 if len(input_shapes) == 1:
--> 432 self.build(input_shapes[0])
433 else:
434 self.build(input_shapes)
/opt/conda/lib/python3.6/site-packages/keras/layers/core.py in build(self, input_shape)
870 name='kernel',
871 regularizer=self.kernel_regularizer,
--> 872 constraint=self.kernel_constraint)
873 if self.use_bias:
874 self.bias = self.add_weight(shape=(self.units,),
/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/opt/conda/lib/python3.6/site-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
247 if dtype is None:
248 dtype = K.floatx()
--> 249 weight = K.variable(initializer(shape),
250 dtype=dtype,
251 name=name,
/opt/conda/lib/python3.6/site-packages/keras/initializers.py in __call__(self, shape, dtype)
216 limit = np.sqrt(3. * scale)
217 return K.random_uniform(shape, -limit, limit,
--> 218 dtype=dtype, seed=self.seed)
219
220 def get_config(self):
/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in random_uniform(shape, minval, maxval, dtype, seed)
4075 seed = np.random.randint(10e6)
4076 return tf.random_uniform(shape, minval=minval, maxval=maxval,
-> 4077 dtype=dtype, seed=seed)
4078
4079
/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py in random_uniform(shape, minval, maxval, dtype, seed, name)
240 shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
241 else:
--> 242 rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2)
243 return math_ops.add(rnd * (maxval - minval), minval, name=name)
244
/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_random_ops.py in random_uniform(shape, dtype, seed, seed2, name)
672 _, _, _op = _op_def_lib._apply_op_helper(
673 "RandomUniform", shape=shape, dtype=dtype, seed=seed, seed2=seed2,
--> 674 name=name)
675 _result = _op.outputs[:]
676 _inputs_flat = _op.inputs
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
607 _SatisfiesTypeConstraint(base_type,
608 _Attr(op_def, input_arg.type_attr),
--> 609 param_name=input_name)
610 attrs[input_arg.type_attr] = attr_value
611 inferred_from[input_arg.type_attr] = input_name
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _SatisfiesTypeConstraint(dtype, attr_def, param_name)
58 "allowed values: %s" %
59 (param_name, dtypes.as_dtype(dtype).name,
---> 60 ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
61
62
TypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64You were correct, @tromgy -- I replicated your error, and have merged your change. Thank you very much for your pull request! 🙏
jonkrohn
commented
5 years ago Hey @tromgy ... out of curiosity, I don't suppose you're testing every Jupyter notebook in this repo (perhaps as you work through the book)? It would be tremendously helpful if you were because we're in the final round of book edits right now. This error you identified, for example, will be corrected within the text.
tromgy
commented
5 years ago Yes, @jonkrohn in fact I do go through every notebook mentioned in the text as I progress through the book. I'm now on chapter 12. Although for the notebooks from chapters prior to 10 I used code from the "old" github repository: https://github.com/illustrated-series/deep-learning-illustrated
jonkrohn
commented
5 years ago That's perfect, thanks 😃
tromgy
commented
5 years ago @jonkrohn, are issues disabled for this repo?
I'm seeing a problem in the generative_adversarial_network notebook. What is the best way to communicate it?
jonkrohn
commented
5 years ago Hi @tromgy ! I certainly haven't deliberately disabled issues. Indeed, I'm rather sure I haven't made any changes to the settings to this repo since the issue you filed last week.
tromgy
commented
5 years ago Last week it was a pull request, not an "issue". Issues are for problems that people see, but don't know how to solve. And that's exactly what I have now with the GAN notebook.
Here's how this repository looks:
the red arrow shows where the Issues tab would be.
And this is how the original repository looks:
so somehow along the migration path the "issues" setting got lost...
In my own repos, when I click the Settings tab, I see this:
so I think it's just the matter of checking this box.
jonkrohn
commented
5 years ago Awesome, @tromgy -- you are correct, as usual! The Issues tab should now be available to you :)
With Python 3.x (and the docker image shows version 3.6.7) we need to explcitly use integer (aka floor) division, otherwise Keras throws:
for instance: