tensorboard

问题 I ran into an apparent circular dependency trying to use log data for TensorBoard during a hyper-parameter search done with Keras Tuner, for a model built with TF2. The typical setup for the latter needs to set up the Tensorboard callback in the tuner's search() method, which wraps the model's fit() method. from kerastuner.tuners import RandomSearch tuner = RandomSearch(build_model, #this method builds the model hyperparameters=hp, objective='val_accuracy') tuner.search(x=train_x, y=train_y,

问题 I ran into an apparent circular dependency trying to use log data for TensorBoard during a hyper-parameter search done with Keras Tuner, for a model built with TF2. The typical setup for the latter needs to set up the Tensorboard callback in the tuner's search() method, which wraps the model's fit() method. from kerastuner.tuners import RandomSearch tuner = RandomSearch(build_model, #this method builds the model hyperparameters=hp, objective='val_accuracy') tuner.search(x=train_x, y=train_y,

问题 I'm trying to implement smart reply tflite model in python or tfjs, but they are using custom ops. Please refer https://github.com/tensorflow/examples/tree/master/lite/examples/smart_reply/android/app/libs/cc. So how to build that custom op separately and use that custom op in python or tfjs? 来源： https://stackoverflow.com/questions/59644961/how-to-use-smart-reply-custom-ops-in-python-or-tfjs

问题 TensorFlow version = 2.0.0 I am following the example of how to use the TensorFlow summary module at https://www.tensorflow.org/api_docs/python/tf/summary; the first one on the page, which for completeness I will paste below: writer = tf.summary.create_file_writer("/tmp/mylogs") with writer.as_default(): for step in range(100): # other model code would go here tf.summary.scalar("my_metric", 0.5, step=step) writer.flush() Running this is fine, and I get event logs that I can view in

问题 Using Python (3.6) / Jupyter (5.7.8) on WIndows 10 .. I have tried many simple examples of trying to generate log files for tensorboard including this: logs_base_dir = "C:/tensorlogs" %load_ext tensorboard.notebook # %tensorboard --port=6006 --logdir {logs_base_dir} os.makedirs(logs_base_dir, exist_ok=True) %tensorboard --port=6008 --logdir {logs_base_dir} a = tf.constant([10]) b = tf.constant([20]) c = tf.add(a,b) with tf.Session() as sess: # or creating the writer inside the session writer

问题 I've installed TensorFlow with Anaconda on OS X Sierra. I didn't´t have any problems during installation. Writing the tipical example: import tensorflow as tf a = tf.constant(5, name="input_a") b = tf.constant(3, name="input_b") c = tf.mul(a, b, name="mul_c") d = tf.add(a, b, name="add_d") e = tf.add(c, d, name="add_e") sess = tf.Session() output = sess.run(e) writer = tf.summary.FileWriter('./my_graph', sess.graph) writer.close() sess.close() The file is created in the appropriate folder,

问题 I have created the following model for training and want to get it visualized on Tensorboard: ## Basic Cell LSTM tensorflow index_in_epoch = 0; perm_array = np.arange(x_train.shape[0]) np.random.shuffle(perm_array) # function to get the next batch def get_next_batch(batch_size): global index_in_epoch, x_train, perm_array start = index_in_epoch index_in_epoch += batch_size if index_in_epoch > x_train.shape[0]: np.random.shuffle(perm_array) # shuffle permutation array start = 0 # start next

问题 I'm trying to use tensorboard on my local machine to read tensorflow logs on S3. Everything works but tensorboard continuously throws the following errors to the console. According to this the reason is that when Tensorflow s3 client checks if directory exists it firstly run Stat on it since s3 have no possibility to check whether directory exists. Then it checks if key with such name exists and fails with such error messages. While this could be a wanted behavior for model serving to look

问题 I am using the following lines of codes to visualise the gradients of an ANN model using tensorboard tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=1, write_graph = True, write_grads =True, write_images = False) tensorboard_callback .set_model(model) %tensorboard --logdir ./Graph I received a warning message saying "WARNING:tensorflow: write_grads will be ignored in TensorFlow 2.0 for the TensorBoard Callback." I get the tensorboard output,

问题 I am using the following lines of codes to visualise the gradients of an ANN model using tensorboard tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=1, write_graph = True, write_grads =True, write_images = False) tensorboard_callback .set_model(model) %tensorboard --logdir ./Graph I received a warning message saying "WARNING:tensorflow: write_grads will be ignored in TensorFlow 2.0 for the TensorBoard Callback." I get the tensorboard output,