DataBloom

why google

My code is

def get_checkpoint_every_epoch(): checkpoint_every_epoch = 'model_checkpoints_every_epoch' checkpoints = ModelCheckpoint(filepath=checkpoint_every_epoch, frequency= 'epoch', save_weights_only=True, verbose=1) return checkpoints def get_checkpoint_best_only(): checkpoint_best_path = 'model_checkpoints_best_only/checkpoint' checkpoint_best= ModelCheckpoint(filepath=checkpoint_best_path, save_weights_only= True, monitor= 'val_accuracy', save_best_only= True, verbose=1) return checkpoint_best def get_early_stopping(): early_stopping= tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3) return early_stopping checkpoint_every_epoch = get_checkpoint_every_epoch() checkpoint_best_only = get_checkpoint_best_only() early_stopping = get_early_stopping() 

Followed by this,

def get_model_last_epoch(model): model_last_epoch_file = tf.train.latest_checkpoint("checkpoints_every_epoch") model.load_weights(model_last_epoch_file) return model def get_model_best_epoch(model): model_best_epoch_file = tf.train.latest_checkpoint("checkpoints_best_only") model.load_weights(model_best_epoch_file) return model model_last_epoch = get_model_last_epoch(get_new_model(x_train[0].shape)) model_best_epoch = get_model_best_epoch(get_new_model(x_train[0].shape)) print('Model with last epoch weights:') get_test_accuracy(model_last_epoch, x_test, y_test) print('') print('Model with best epoch weights:') get_test_accuracy(model_best_epoch, x_test, y_test) 

This is where, I get this error:

​

AttributeError Traceback (most recent call last) <ipython-input-18-b6d169507ca4> in <module> 3 # Verify that the second has a higher validation (testing) accuarcy. 4 ----> 5 model_last_epoch = get_model_last_epoch(get_new_model(x_train[0].shape)) 6 model_best_epoch = get_model_best_epoch(get_new_model(x_train[0].shape)) 7 print('Model with last epoch weights:') <ipython-input-17-4c8cba016afe> in get_model_last_epoch(model) 12 model_last_epoch_file = tf.train.latest_checkpoint("checkpoints_every_epoch") 13 ---> 14 model.load_weights(model_last_epoch_file) 15 16 return model /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in load_weights(self, filepath, by_name) 179 raise ValueError('Load weights is not yet supported with TPUStrategy ' 180 'with steps_per_run greater than 1.') --> 181 return super(Model, self).load_weights(filepath, by_name) 182 183 @trackable.no_automatic_dependency_tracking /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in load_weights(self, filepath, by_name) 1137 format. 1138 """ -> 1139 if _is_hdf5_filepath(filepath): 1140 save_format = 'h5' 1141 else: /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _is_hdf5_filepath(filepath) 1447 1448 def _is_hdf5_filepath(filepath): -> 1449 return (filepath.endswith('.h5') or filepath.endswith('.keras') or 1450 filepath.endswith('.hdf5')) 1451 AttributeError: 'NoneType' object has no attribute 'endswith' 

What does it mean? Sorry, I’m just a newbie, need some
enlightenment. I wish you a merry christmas. Thanks a lot!

So I am following a YouTube series here: https://www.youtube.com/watch?v=CA0PQS1Rj_4

And this person also posted their code on GitHub:
https://github.com/musikalkemist/Deep-Learning-Audio-Application-From-Design-to-Deployment/tree/master/4-%20Making%20Predictions%20with%20the%20Speech%20Recognition%20System

(I removed the model.h5 and data.json since I wanted to use a
model generated on my own PC)

I run the train.py which trains the model and get this as a
result: https://pastebin.com/mZSXK25v

When i test the “down.wav”, it predicts “right”: https://pastebin.com/Up8EvNyc

When I tested “left.wav”, it predicts “down”: https://pastebin.com/vzWzTV4X

How come I get different results, in fact completely wrong
results no matter what I test, despite getting a 0.9358
accuracy?

I was doing the assignment for the “Model validation on the Iris
dataset”.

I get this error: “Error when checking input: expected
dense_input to have shape (135,) but got array with shape (4,)”.
How do I overcome this problem?

I posted this question at
https://stackoverflow.com/questions/65441320/how-to-overcome-the-wrong-dimension-issue.

​

I wish someone can highlight my error. Pardon me for asking this
question. I’m still a newbie. Making basic errors here and there.
But anyway, I’d like to wish everyone a happy Christmas Day
ahead.

Hi there,

I am a beginner and struggling a bit to understand what are the
‘protos’ in TF Object Detection?

Why do we need them here?

Also, while setting up the TF API we need to download and
compile protocol buffers.

There is also a ‘protos’ folder when one downloads the object
detection module – could anyone please explain me what are those
and what is the relationship between them?

This would be of immense help!

Thanks!

I am running a tensorflow application that sets inter_op,
intra_op and OMP_NUM_THREADS, however, it completely ignores these
settings and seems to run with the defaults. Here’s how I’m setting
them:

​

 import tensorflow as tf print('Using Thread Parallelism: {} NUM_INTRA_THREADS, {} NUM_INTER_THREADS, {} OMP_NUM_THREADS'.format(os.environ['NUM_INTRA_THREADS'], os.environ['NUM_INTER_THREADS'], os.environ['OMP_NUM_THREADS'])) session_conf = tf.compat.v1.ConfigProto(inter_op_parallelism_threads=int(os.environ['NUM_INTER_THREADS']), intra_op_parallelism_threads=int(os.environ['NUM_INTRA_THREADS'])) sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf) tf.compat.v1.keras.backend.set_session(sess) I have validated that it's reading the right values (the print prints the values as expected). I have also tried with other Tensorflow 2 versions with no success. I am at a loss as to what I'm doing wrong. Version Info: tensorflow 2.2.0 py37_2 intel tensorflow-base 2.2.0 0 intel tensorflow-estimator 2.2.0 pyh208ff02_0 keras 2.4.3 0 keras-base 2.4.3 py_0 keras-preprocessing 1.1.0 py_1