Month: February 2021

I want to get a head combined with a body preserving art style. Or that head generating a body around it (image completion) using a trained model from a dataset.
In short:
Input 1: Single image of a head X in style A
Input 2: Single image of a body Y in style B
Data I have: a lot of same-size images of characters (with heads and bodies) in style B
Output: Single image of body Y with head X in style B (rather seamless, but no need to be perfect)

Couple of days ago I ran into a big list of Google collabs but most don’t describe properly what they do, and the few that do, won’t do what I need.
What application or project do you recommend for this? If it’s a project, will it run on CPU? I’ve got Windows and can install Anaconda.
Bottom line: is there in the very least an application or project that just gets head X into body Y regardless of style?

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My experience so far:

In RunwayML I trained a StyleGAN model with art portraits of the same style and size, but after that, it does only image synthesis (creates new random portraits with the same style)😒 . It won’t take an input photo and turn it into a portrait in that style. At a fairly expensive subscription and machine-use fee, I think it offered very little bang for the buck.

Artbreeder portrait tools are better and fees are quite lower, and include using uploaded photos as inputs, mixing photos and creating new “genes” for the faces that may include an art style. However, its portrait tools are limited to heads only, from the neck up. It’s also terrible at preserving features such as piercings, tattoos, horns, elf ears etc.

Big thanks to anyone who might help.

I defined a model using tf.keras (v2.3.0) and I want to perfrom quantization aware training in this way:

import tensorflow as tf from tensorflow.keras import layers #i tried to replace with tf.python.keras.layers.VersionAwareLayers import tensorflow_model_optimization as tfmot def build_model(): inputs = tf.keras.Input() x = layers.Conv2D(24, 5, 2, 'relu')(inputs) x = layers.BatchNormalization()(x) # more layers... logits = layers.Softmax()(x) model = tf.keras.Model(inputs=inputs, outputs=logits) return model model = build_model() # training code q_aware_model = tfmot.quantization.keras.quantize_model(model) 

I get this error:

RuntimeError: Layer batch_normalization_2:<class ‘tensorflow.python.keras.layers.normalization_v2.BatchNormalization’> is not supported. You can quantize this layer by passing a ‘tfmot.quantization.keras.QuantizeConfig’ instance to the ‘quantize_anotate_layer’ API

However, if I define the model as a keras MobilenetV2, which contains the same BatchNormalization layer, everything works fine. Where is the difference? How can I fix this problem?

I was working off some github code where I’m training a model to recognize laughter. This particular bit of code is giving me problems:

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from keras.models import Sequential from keras.layers import Dense, BatchNormalization, Flatten lr_model = Sequential() # lr_model.add(keras.Input((None, 128))) lr_model.add(BatchNormalization(input_shape=(10, 128))) lr_model.add(Flatten()) lr_model.add(Dense(1, activation='sigmoid')) # try using different optimizers and different optimizer configs lr_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) batch_size=32 CV_frac = 0.1 train_gen = data_generator(batch_size,'../Data/bal_laugh_speech_subset.tfrecord', 0, 1-CV_frac) val_gen = data_generator(128,'../Data/bal_laugh_speech_subset.tfrecord', 1-CV_frac, 1) rec_len = 18768 lr_h = lr_model.fit_generator(train_gen,steps_per_epoch=int(rec_len*(1-CV_frac))//batch_size, epochs=100, validation_data=val_gen, validation_steps=int(rec_len*CV_frac)//128, verbose=0, callbacks=[TQDMNotebookCallback()]) 

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I get the following error:

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--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-15-9eced074de11> in <module> 7 rec_len = 18768 8 ----> 9 lr_h = lr_model.fit_generator(train_gen,steps_per_epoch=int(rec_len*(1-CV_frac))//batch_size, epochs=100, 10 validation_data=val_gen, validation_steps=int(rec_len*CV_frac)//128, 11 verbose=0, callbacks=[TQDMNotebookCallback()]) ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch) 1845 'will be removed in a future version. ' 1846 'Please use `Model.fit`, which supports generators.') -> 1847 return self.fit( 1848 generator, 1849 steps_per_epoch=steps_per_epoch, ~/.local/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) 1103 logs = tmp_logs # No error, now safe to assign to logs. 1104 end_step = step + data_handler.step_increment -> 1105 callbacks.on_train_batch_end(end_step, logs) 1106 if self.stop_training: 1107 break ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py in on_train_batch_end(self, batch, logs) 452 """ 453 if self._should_call_train_batch_hooks: --> 454 self._call_batch_hook(ModeKeys.TRAIN, 'end', batch, logs=logs) 455 456 def on_test_batch_begin(self, batch, logs=None): ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook(self, mode, hook, batch, logs) 294 self._call_batch_begin_hook(mode, batch, logs) 295 elif hook == 'end': --> 296 self._call_batch_end_hook(mode, batch, logs) 297 else: 298 raise ValueError('Unrecognized hook: {}'.format(hook)) ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py in _call_batch_end_hook(self, mode, batch, logs) 314 self._batch_times.append(batch_time) 315 --> 316 self._call_batch_hook_helper(hook_name, batch, logs) 317 318 if len(self._batch_times) >= self._num_batches_for_timing_check: ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook_helper(self, hook_name, batch, logs) 358 if numpy_logs is None: # Only convert once. 359 numpy_logs = tf_utils.to_numpy_or_python_type(logs) --> 360 hook(batch, numpy_logs) 361 362 if self._check_timing: ~/.local/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py in on_train_batch_end(self, batch, logs) 708 """ 709 # For backwards compatibility. --> 710 self.on_batch_end(batch, logs=logs) 711 712 @doc_controls.for_subclass_implementers ~/env/py385/lib/python3.8/site-packages/keras_tqdm/tqdm_callback.py in on_batch_end(self, batch, logs) 115 self.inner_count += update 116 if self.inner_count < self.inner_total: --> 117 self.append_logs(logs) 118 metrics = self.format_metrics(self.running_logs) 119 desc = self.inner_description_update.format(epoch=self.epoch, metrics=metrics) ~/env/py385/lib/python3.8/site-packages/keras_tqdm/tqdm_callback.py in append_logs(self, logs) 134 135 def append_logs(self, logs): --> 136 metrics = self.params['metrics'] 137 for metric, value in six.iteritems(logs): 138 if metric in metrics: KeyError: 'metrics' 

All the other KeyError ‘metrics’ problems I’ve googled have to do with something else called livelossplot. Any help will be much appreciated!

Hello there. I’m thinking of using Genetic Algorithm to tune Hyper-parameters of Neural Networks.

Apart form this paper and this blog blog, I don’t find anything that relates with the topic. However there are many GA libraries such as PyGAD etc, but they only apply GA onto weights to fine tune the model instead of finding the best hyper-parameters.

By any chance, anyone here tried anything like this before, as in using GA to find the best hyperparameter in a Tensorflow/Keras Model? Mind share your thoughts?

thanks!

I’m trying to create a DNN classifier and am running into the following error:

Invalid argument: assertion failed: [Labels must be <= n_classes – 1] [Condition x <= y did not hold element-wise:] [x (head/losses/labels:0) = ] [[3][2][4]…] [y (head/losses/check_label_range/Const:0) = ] [4]

I am following the general structure from https://www.tensorflow.org/tutorials/estimator/premade and am not sure what I’m doing wrong.

In my data, there are 255 columns and 4 possible classifications for each row.

I have excluded the imports

training_data = pd.read_csv(data_file)
target_data = pd.read_csv(target_file)
train_y = training_data.pop(‘StateCode’)
target_y = target_data.pop(‘StateCode’)
def input_fn(features, labels, training=True, batch_size=256):
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()

return dataset.batch(batch_size)
my_feature_columns = []
for key in training_data.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
labels = list(training_data.columns)
print(labels)
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 30 and 10 nodes respectively.
hidden_units=[30, 10],
# The model must choose between 4 classes.
n_classes=4)

classifier.train(
input_fn=lambda: input_fn(training_data, train_y, training=True),
steps=5000)

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Any help would be appreciated.

Hi everyone,

I am trying to use a model saved in .h5 format to analyze a video stream and identify the speed in real time. How do I implement this in Python?

Thank you in advance!

Hey!

I’m following this installation guide for object detection using tensorflow 2, my goal is to train a CNN using my GPU. Tensorflow seems to recognize it after the GPU support section and everything runs smoothly. However, after I install the Object Detection API, tensorflow just starts ignoring it and runs on CPU. Any help would be deeply apreciated, thanks!

After several hours of Google searching, I cannot find a straightforward answer. I’m not working with the W model, so my RPi doesn’t have a wireless adapter. I have found results that all involve cloning repos on the RPi itself, but I have no clue how to go about it. Do I need a Raspberian build that already has TF built-in to it? Or is there a way I can scp all the files over to my Pi?

I’ve tried what’s listed here, but instead downloading TF and its dependencies on my local machine, but didn’t know what to do in regards to the file path.

If anyone knows of a good guide or knows how to do this…please let me know. Thanks!

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