| # Working example for my blog post at: | |
| # https://danijar.github.io/structuring-your-tensorflow-models | |
| import functools | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| def doublewrap(function): | |
| """ | |
| A decorator decorator, allowing to use the decorator to be used without | |
| parentheses if no arguments are provided. All arguments must be optional. | |
| """ | |
| @functools.wraps(function) | |
| def decorator(*args, **kwargs): | |
| if len(args) == 1 and len(kwargs) == 0 and callable(args[0]): | |
| return function(args[0]) | |
| else: | |
| return lambda wrapee: function(wrapee, *args, **kwargs) | |
| return decorator | |
| @doublewrap | |
| def define_scope(function, scope=None, *args, **kwargs): | |
| """ | |
| A decorator for functions that define TensorFlow operations. The wrapped | |
| function will only be executed once. Subsequent calls to it will directly | |
| return the result so that operations are added to the graph only once. | |
| The operations added by the function live within a tf.variable_scope(). If | |
| this decorator is used with arguments, they will be forwarded to the | |
| variable scope. The scope name defaults to the name of the wrapped | |
| function. | |
| """ | |
| attribute = '_cache_' + function.__name__ | |
| name = scope or function.__name__ | |
| @property | |
| @functools.wraps(function) | |
| def decorator(self): | |
| if not hasattr(self, attribute): | |
| with tf.variable_scope(name, *args, **kwargs): | |
| setattr(self, attribute, function(self)) | |
| return getattr(self, attribute) | |
| return decorator | |
| class Model: | |
| def __init__(self, image, label): | |
| self.image = image | |
| self.label = label | |
| self.prediction | |
| self.optimize | |
| self.error | |
| @define_scope(initializer=tf.contrib.slim.xavier_initializer()) | |
| def prediction(self): | |
| x = self.image | |
| x = tf.contrib.slim.fully_connected(x, 200) | |
| x = tf.contrib.slim.fully_connected(x, 200) | |
| x = tf.contrib.slim.fully_connected(x, 10, tf.nn.softmax) | |
| return x | |
| @define_scope | |
| def optimize(self): | |
| logprob = tf.log(self.prediction + 1e-12) | |
| cross_entropy = -tf.reduce_sum(self.label * logprob) | |
| optimizer = tf.train.RMSPropOptimizer(0.03) | |
| return optimizer.minimize(cross_entropy) | |
| @define_scope | |
| def error(self): | |
| mistakes = tf.not_equal( | |
| tf.argmax(self.label, 1), tf.argmax(self.prediction, 1)) | |
| return tf.reduce_mean(tf.cast(mistakes, tf.float32)) | |
| def main(): | |
| mnist = input_data.read_data_sets('./mnist/', one_hot=True) | |
| image = tf.placeholder(tf.float32, [None, 784]) | |
| label = tf.placeholder(tf.float32, [None, 10]) | |
| model = Model(image, label) | |
| sess = tf.Session() | |
| sess.run(tf.initialize_all_variables()) | |
| for _ in range(10): | |
| images, labels = mnist.test.images, mnist.test.labels | |
| error = sess.run(model.error, {image: images, label: labels}) | |
| print('Test error {:6.2f}%'.format(100 * error)) | |
| for _ in range(60): | |
| images, labels = mnist.train.next_batch(100) | |
| sess.run(model.optimize, {image: images, label: labels}) | |
| if __name__ == '__main__': | |
| main() |
Hi @lfwin, I don't know why your debugger can't step into setattr() statements. Maybe because it's a built-in function. Regarding your second question, that's the intention of the decorator. Please read my blog post for an explanation, the URL is at the beginning of the file.
Hi -- is there a license on this code? I'd like to use it in a package that is Apache 2.0 licensed. Happy to have a dependency on it if you prefer to package it up instead.
Great design! Any idea how it works with tensorflow saver? How would you restore a checkpoint of a session that's baked into this class?
@danijar, is it OK to use this code in a project licensed under Apache 2.0?
Hi -- will "define_scope" work when the wrapped function have argument others than self? for example, if i try
@define_scope
def prediction(self, batch_size):
...
and I get "TypeError: prediction() missing 1 required positional argument"
@pucktada, you may want to redefine "def decorator(self)" to "def decorator(self, *args, **kwargs)". Likewise for function(self, *args, **kwargs) inside the wrapper function
Hey, I tried extending the idea of the decorator here to set the variable names as well
Thanks for this gist @danijar Could you please explain how the feed_dict in line 87 (or any similar line) works? Are the feed_dict keys same as the args in init ? Thanks.
I got that part! But I have another question now - Is it possible to define the attributes such as "optimize" in another file that imports this class? If yes, how? Thanks.
Hello, I changed the code a little bit.
I want to change the cost function dynamically in each iteration, the code is here https://github.com/jren2017/accessAccuracyFromLossFunc/blob/master/from_gist_example.py
I want to access the error of last iteration, use the error to calculate the cross-entropy, this sounds weird, but just an example.
Hope you can help have a look.
changed 65th row of the code:
logprob = tf.log(self.prediction + 1e-12) *(1-current_error) #Here changed ????????????
I get the error message below when I run the example. How do I fix it? It appears to be missing the _gru_ops library.
Great article. Thank you.
File "C:\ProgramData\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\framework\load_library.py", line 56, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
NotFoundError: C:\ProgramData\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\contrib\rnn\python\ops_gru_ops.so not found
hi,
I debuged this code and found that:
1 if set breakpoint at line number 15 'setattr(self, attribute, function(self))' which is execute in Model initilization ' model = Model(data, target)' at line number 53, can step in this line, but if set breakpoint before excecution this set attribute line such as line num 52 or line num 25 and can not step into this set attribute line, why?
2 after executing to set attribute self.optimize at line number 25 and befor excuting self.error, self has three attributes, self._optimize, self._error, self._prediction, i don't know how this be generated?