Month: May 2021

I am a bit new to the TensorFlow. I have following questions

1. How to select which version to use.
2. How different are the two versions.
3. what could be the best way to learn TensorFlow( are learning TF1 and learning TF2 completely different things?)

I would really appreciate any help. Thank you

Hi,

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Could anyone please give me an example of how to batch the calculation when using tf.image.resize? Resizing all the images (20000) in my dataset at once crashes my system. I’m trying to upscale my images. I couldn’t find the documentation or examples for it.

Sorry for the mistake in Question,

Is the combination of high batch size and low epoch the same as that of low batch size and high epoch?

Hey! I was just wondering …

If I try to generate High-resolution(128,128,3) images from Low resolution(32,32,3) images using GAN on a dataset of 1000 Images by following two methods :

Result_A: I run 100,000 epoch and batch_size =1 then my model will train on a total of 100,000 images. And this one image per epoch is selected randomly.

Result_B: I run 100 epochs on batch_size = 1000, then also my model trains on a total of 100,000 images.

Would Result_A be similar to Result_B? If not, why?

The differentiating factor is that in Result_B my model goes through all images, but it might not go through all images. Will this create a significant difference?

Hello,

My code :

https://gofile.io/d/mTlna9

i’m beginner with tensorflow, and i’m trying to make a deeplab implementation in tensorflow 2.

I run my notebook and i don’t have error during the fit when i use run_eagerly = True, but with this option the loss stay at nan

And when i don’t use run_eagerly, i have this error :

————————————————————————— ValueError Traceback (most recent call last) <ipython-input-16-f5ffc11fb617> in <module> 133 dataset_entrain = dataset_entrain.shuffle(buffer_size=10).batch(batch_size=1) 134 –> 135 model.fit(dataset_entrain, epochs=10, callbacks=[ModelCheckpoint(‘policy_seg_{epoch}.h5’)]) 136 137 ~/envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing) 1098 _r=1): 1099 callbacks.on_train_batch_begin(step) -> 1100 tmp_logs = self.train_function(iterator) 1101 if data_handler.should_sync: 1102 context.async_wait() ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds) 826 tracing_count = self.experimental_get_tracing_count() 827 with trace.Trace(self._name) as tm: –> 828 result = self._call(*args, **kwds) 829 compiler = “xla” if self._experimental_compile else “nonXla” 830 new_tracing_count = self.experimental_get_tracing_count() ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 886 # Lifting succeeded, so variables are initialized and we can run the 887 # stateless function. –> 888 return self._stateless_fn(*args, **kwds) 889 else: 890 _, _, _, filtered_flat_args = ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs) 2939 with self._lock: 2940 (graph_function, -> 2941 filtered_flat_args) = self._maybe_define_function(args, kwargs) 2942 return graph_function._call_flat( 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 3359 3360 self._function_cache.missed.add(call_context_key) -> 3361 graph_function = self._create_graph_function(args, kwargs) 3362 self._function_cache.primary[cache_key] = graph_function 3363 ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3194 arg_names = base_arg_names + missing_arg_names 3195 graph_function = ConcreteFunction( -> 3196 func_graph_module.func_graph_from_py_func( 3197 self._name, 3198 self._python_function, ~/envs/project/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes) 988 _, original_func = tf_decorator.unwrap(python_func) 989 –> 990 func_outputs = python_func(*func_args, **func_kwargs) 991 992 # invariant: `func_outputs` contains only Tensors, CompositeTensors, ~/envs/project/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds) 632 xla_context.Exit() 633 else: –> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds) 635 return out 636 ~/envs/project/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 975 except Exception as e: # pylint:disable=broad-except 976 if hasattr(e, “ag_error_metadata”): –> 977 raise e.ag_error_metadata.to_exception(e) 978 else: 979 raise ValueError: in user code: /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:805 train_function * return step_function(self, iterator) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:795 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica return fn(*args, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:788 run_step ** outputs = model.train_step(data) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:754 train_step y_pred = self(x, training=True) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1012 __call__ outputs = call_fn(inputs, *args, **kwargs) <ipython-input-16-f5ffc11fb617>:83 call ** x = keras.layers.Conv2D(64, 7, strides=2, padding=”same”)(inputs) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1008 __call__ self._maybe_build(inputs) /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:2710 _maybe_build self.build(input_shapes) # pylint:disable=not-callable /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:198 build self.kernel = self.add_weight( /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:623 add_weight variable = self._add_variable_with_custom_getter( /envs/project/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py:805 _add_variable_with_custom_getter new_variable = getter( /envs/project/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_utils.py:130 make_variable return tf_variables.VariableV1( /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:260 __call__ return cls._variable_v1_call(*args, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:206 _variable_v1_call return previous_getter( /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:67 getter return captured_getter(captured_previous, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator return next_creator(**kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:67 getter return captured_getter(captured_previous, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator return next_creator(**kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:67 getter return captured_getter(captured_previous, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3332 creator return next_creator(**kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/ops/variables.py:67 getter return captured_getter(captured_previous, **kwargs) /envs/project/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:730 invalid_creator_scope raise ValueError( ValueError: tf.function-decorated function tried to create variables on non-first call.

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You can find my code in the link https://gofile.io/d/mTlna9 , what can i do to debug my code ?

Thank you for your advice

Hey, I am interested in specifically learning tensorflow for JavaScript. I have learned the basics already and made some simple things like a model that predicts where crypto prices are going, but I keep running into walls trying to make things more complex.

I understand that all tensorflow works generally the same, but it would be cool if the book was JS focused (not a requirement). I would also appreciate a book that didn’t just throw a ton of formulas at you and instead took time to explain them. I have only taken up to calculus in college math so far. Thanks.

I’m trying to install Tensorflow 2.4.1 in a Docker container running Ubuntu 20.04 on my M1 MacBook Pro. I keep getting an error that says qemu: uncaught target signal 6 (Aborted) – core dumped

I’ve seen that this is common issue. I can get it to install and run fine on my base machine, it’s just when installing in the container that I have an issue.

Is there any way to install this properly? Thanks in advance!

I have a tensor in TensorFlow 2.5 where the goal is to set the smallest sum of magnitude based on p% of ‘slices’ to zeros. The sample code to achieve this is as follows:

# create example tensor- input_shape = (1, 4, 4, 6) y = tf.random.normal(input_shape) 

The goal is to find and remove the smallest (say) p = 20% of the values from the last index ‘6’ based on the sum of their magnitudes. floor(20% of 6) = 1, i.e., after removing 20% of smallest magnitude summation of values, 5 of the 4×4 ‘slices’ should have non-zero values while the 6th slice should be equal to zero.

 # Sum magnitude values- filter_sum = tf.math.reduce_sum(input_tensor = tf.math.abs(y), axis = [1, 2], keepdims = False) filter_sum.shape # TensorShape([1, 6]) indices = tf.argsort(filter_sum) indices.shape # TensorShape([1, 6]) indices.numpy() # array([[0, 1, 4, 2, 5, 3]]) # 20% of 6 values = 1 math.floor(0.2 * 6) # 1 

In this example, the smallest 4×4 ‘slice’ in ‘y’ is the first slice as computed by ‘indices’. How do I proceed?

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Thanks

Sorry if my question is misleading. I have built and trained a model which can determine what character is the input image (the image is a handwritten letter). I’d like to do not only 1 character, but let’s say a word or a text. Do I have to write my own code which segments the input image of a text/word into characters? If so where should I begin? Or is there a working segmenting model which I could just feed my trained model into then it would do the work?

Oh and the trained model consists of old latin handwritten characters.

I’m really new to this topic so any help would be appreciated! Thank you!