DataBloom

Hi everybody! It’s my first post here and I’m a beginner with TF too.

I’m trying to implement deep q-learning on the Connect 4 game. Reading some examples on the internet, I’ve understood that using the decorator tf.function can speed up a lot the training, but it has no other effect than performance.

Actually, I have noticed a different behavior in my function:

@tf.function # do I need it? def _train_step(self, boards_batch, scores_batch): with tf.GradientTape() as tape: batch_predictions = self.model(boards_batch, training=True) loss_on_batch = self.loss_object(scores_batch, batch_predictions) gradients = tape.gradient(loss_on_batch, self.model.trainable_variables) self.optimizer.apply_gradients(zip( gradients, self.model.trainable_variables )) self.loss(loss_on_batch) 

In particular, if I train without tf.function the agent is not learning anything and it performs as the Random agent. Instead, if I use tf.function the agent easily beats the random agent after only 1000 episodes.

Do you have any idea why is this happening? Do I have misunderstood something about tf.function?

If I try to remove tf.function from TF example notebooks nothing changes a part from the performance, as I expect from my understanding of tf.function.

Hi, i am just starting with Tensorflow for my AI and i ran into an error i don’t know how to solve

[2021-09-06 21:55:50.461476: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2021-09-06 21:55:51.050032: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2781 MB memory: -> device: 0, name: NVIDIA GeForce GTX 970, pci bus id: 0000:01:00.0, compute capability: 5.2

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasoptimizer_v2optimizer_v2.py:355: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.

warnings.warn(

2021-09-06 21:55:51.508673: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)

Epoch 1/200

Traceback (most recent call last):

File “C:UsersGamereclipse-workspaceAItraining_jarvis.py”, line 69, in <module>

model.fit(np.array(training_1), np.array(training_2), epochs=200, batch_size=5, verbose=2)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py“, line 1184, in fit

tmp_logs = self.train_function(iterator)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 885, in __call__

result = self._call(*args, **kwds)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 933, in _call

self._initialize(args, kwds, add_initializers_to=initializers)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 759, in _initialize

self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3066, in _get_concrete_function_internal_garbage_collected

graph_function, _ = self._maybe_define_function(args, kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3463, in _maybe_define_function

graph_function = self._create_graph_function(args, kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerfunction.py“, line 3298, in _create_graph_function

func_graph_module.func_graph_from_py_func(

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworkfunc_graph.py”, line 1007, in func_graph_from_py_func

func_outputs = python_func(*func_args, **func_kwargs)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythoneagerdef_function.py”, line 668, in wrapped_fn

out = weak_wrapped_fn().__wrapped__(*args, **kwds)

File “C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworkfunc_graph.py”, line 994, in wrapper

raise e.ag_error_metadata.to_exception(e)

TypeError: in user code:

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C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:853 train_function *

return step_function(self, iterator)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:842 step_function **

outputs = model.distribute_strategy.run(run_step, args=(data,))

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:1286 run

return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:2849 call_for_each_replica

return self._call_for_each_replica(fn, args, kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythondistributedistribute_lib.py:3632 _call_for_each_replica

return fn(*args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:835 run_step **

outputs = model.train_step(data)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginetraining.py:787 train_step

y_pred = self(x, training=True)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginebase_layer.py:1037 __call__

outputs = call_fn(inputs, *args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginesequential.py:369 call

return super(Sequential, self).call(inputs, training=training, mask=mask)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginefunctional.py:414 call

return self._run_internal_graph(

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginefunctional.py:550 _run_internal_graph

outputs = node.layer(*args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasenginebase_layer.py:1037 __call__

outputs = call_fn(inputs, *args, **kwargs)

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:212 call

output = control_flow_util.smart_cond(training, dropped_inputs,

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskerasutilscontrol_flow_util.py:105 smart_cond

return tf.__internal__.smart_cond.smart_cond(

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packagestensorflowpythonframeworksmart_cond.py:56 smart_cond

return true_fn()

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:208 dropped_inputs

noise_shape=self._get_noise_shape(inputs),

C:UsersGamerAppDataLocalProgramsPythonPython39libsite-packageskeraslayerscore.py:197 _get_noise_shape

for i, value in enumerate(self.noise_shape):

​

TypeError: ‘int’ object is not iterable]

​

I guess it’s about the model.fit line but i am not sure, for reference here is a bit of my code:

[training_1 = list(training_ai[:,0])

training_2 = list(training_ai[:,1])

​

model = Sequential()

model.add(Dense(128, input_shape=(len(training_1[0]),),activation=’relu’))

model.add(Dropout(0,5))

model.add(Dense(64, activation = ‘relu’))

model.add(Dropout(0,5))

model.add(Dense(len(training_2[0]),activation=’softmax’))

​

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss=’categorical_crossenropy’, optimizer=sgd, metrics=[‘accuracy’])

​

model.fit(np.array(training_1), np.array(training_2), epochs=200, batch_size=5, verbose=2)]

​

I would be happy if you could help me with this Error

Hey, all.

I’m quite new to TensorFlow and machine learning in general, and I would like to know if there are any wonderful resources out there holding large data sets to train on.

My end goal is to train an algorithm to identify dead pixels in images, so if there are any resources that specifically contain image sets or, if I’m incredibly lucky, contain image sets with dead pixels, those would be ideal.

Thanks in advance.

Hey all!

​

I am using an RGB dataset for my x train and the loss is calculated in a dynamic loss function that gets the distances of pairs and compares them against the ideal distance dist_train. Here is the model:

class MyModel(Model): def __init__(self): super(MyModel, self).__init__() self.d1 = Dense(3, activation='relu') self.flatten = Flatten() self.d2 = Dense(3, activation='relu') self.d3 = Dense(2) def call(self, x): x = self.d1(x) x = self.flatten(x) x = self.d2(x) return self.d3(x) # Create an instance of the model model = MyModel() optimizer = tf.keras.optimizers.Adam() train_loss = tf.keras.metrics.Mean(name='train_loss') test_loss = tf.keras.metrics.Mean(name='test_loss') @tf.function def train_step(rgb): with tf.GradientTape() as tape: predictions = model(rgb, training=True) loss = tf_function(predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) 

Here is the loss function and the tf.function wrapping it:

def mahal_loss(output): mahal = sp.spatial.distance.pdist(output, metric='mahalanobis') mahal = sp.spatial.distance.squareform(mahal, force='no', checks=True) new_distance = [] mahal = np.ma.masked_array(mahal, mask=mahal==0) for i in range(len(mahal)): pw_dist = mahal[i, indices_train[i]] new_distance.append(pw_dist) mahal_loss = np.mean((dist_train - new_distance)**2) return mahal_loss @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)]) def tf_function(pred): y = tf.numpy_function(mahal_loss, [pred], tf.float32) return y 

Running the model:

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EPOCHS = 5 for epoch in range(EPOCHS): train_loss.reset_states() test_loss.reset_states() for i in x_train: train_step(i) print( f'Epoch {epoch + 1}, ' f'Loss: {train_loss.result()}, ' f'Test Loss: {test_loss.result()}, ' ) 

I believe the reason I am running into problems lies in the dynamic loss function, as I need to calculate the distance between certain pairs to get the results I expect. This means that inside the loss function I have to calculate the mahalanobis distance of each pair to get the ones I will compare against the correct distances. The error I get is the following:

 in user code: <ipython-input-23-0e975da5cbc2>:15 train_step * optimizer.apply_gradients(zip(gradients, model.trainable_variables)) C:Anaconda3envscolour_envlibsite-packageskerasoptimizer_v2optimizer_v2.py:622 apply_gradients ** grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars) C:Anaconda3envscolour_envlibsite-packageskerasoptimizer_v2utils.py:72 filter_empty_gradients raise ValueError("No gradients provided for any variable: %s." % ValueError: No gradients provided for any variable: ['my_model/dense/kernel:0', 'my_model/dense/bias:0', 'my_model/dense_1/kernel:0', 'my_model/dense_1/bias:0', 'my_model/dense_2/kernel:0', 'my_model/dense_2/bias:0']. 

I have following inputs to be train on CNN.

x = np.array(Images)

y = [ [[0]], [[76., 5., 9., 1., 0., 0.], [54., 4., 10., 51.]] ]

Since the ‘y’ input is a n-dimensions array of non-uniform sizes, I used RaggedTensor to represent ‘y’ input and fed it to the network.

y = tf.ragged.constant(y)

cnn_model.fit(x, y, epochs = 10, batch_size=32, validation_split=0.30)

I am receiving following error:

ValueError: validation_split is only supported for Tensors or NumPy arrays, found following types in the input: [<class ‘tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor’>]

If I convert ‘y’ to numpy.ndarray and fit it to the model, I get following error,

cnn_model.fit(x, y.numpy(), epochs = 10, batch_size=32, validation_split=0.30)

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).

I would want to train this input ‘y’ of n-dimensional array to the model, kindly suggest which datatype representation would be suitable regarding this.

I’m trying to build a model and convert it to use in TensorFlow Lite for Microcontrollers. I’m having an issue where every Keras model I generate contains a REDUCE_PROD operator (even a completely basic model consisting of a single Dense(1) layer). However, the TF Lite for Microcontrollers runtime doesn’t support the REDUCE_PROD operator and flags an error upon attempting to load the model.

Is there a way I can exclude this operator when generating a model? Am I missing something?

Thanks!

I have a model that uses R2 as a metric. Since AFAIK there isn’t one natively implemented in TF, I use the one from the tensorflow-addons package. However, when I try to load this model after saving, it fails with the error:

type of argument “y_shape” must be a tuple; got list instead

Here is a minimal working example that produces this error:

from tensorflow.keras.models import load_model, Sequential from tensorflow.keras.layers import Dense, Input import tensorflow as tf import tensorflow_addons as tfa model = Sequential() model.add(Input(5)) model.add(Dense(5)) model.add(Dense(5)) model.compile(metrics = [tfa.metrics.RSquare(y_shape=(5,))]) model.save('test_model.h5') model = load_model('test_model.h5') 

RSquare works fine during training but I need to be able to load the model later (and load models I have already saved). I have tried using the custom_objects argument to load_model but this makes no difference. Any suggestions?

Thanks in advance!

I am attempting to train a 3 layer neural network that predicts maximum and minimum survival duration. The final layer has two outputs (corresponding to a prediction of maximum/minimum survival) and I have written a custom loss function. However I have realised that I need to apply the loss differently depending on which node I am evaluating.

What would be the best way of approaching this? Would I be better off training two separate models to predict maximum and minimum survival?

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Thank you