tensorflow2.0

问题 Based on this post, we can change the tf.variable threshold = 3 a = np.array([1,2,3,4,5,6]) b = tf.Variable(a) b = tf.where(b >= threshold, 199, b) Will b=tf.where(b>=3, 199, b) be plugin to the computational graph and affect the gradient of b in the back propragration? Or more general questions: What types of operations will/will not plugin the computational graph in the Tensorflow 2 ? 来源： https://stackoverflow.com/questions/65449945/what-types-of-operations-will-will-not-plugin-the

问题 I am trying to create a tensor board in Jupyter anaconda the following way. The error occurs when write_images = True , otherwise, this code works fine. Any reason why this happens? log_dir="logs\\fit\\" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph = True, write_images = False update_freq = 'epoch' profile_batch = 3, embeddings_freq=1 ) And I get UnknownError: Failed to rename: logs

问题 I am just wondering what is the difference between the "classification" and "detection" fine tuning types for the available checkpoints in the object detection API. Are they both eligible to train novel classes? The checkpoints obtained from such training, can be further trained with the very same pipeline.config, or does it need to have a different fine tuning type? EDIT to make the question clearer, one can take as reference the ckpts mentioned in https://github.com/tensorflow/models/blob

问题 I am just wondering what is the difference between the "classification" and "detection" fine tuning types for the available checkpoints in the object detection API. Are they both eligible to train novel classes? The checkpoints obtained from such training, can be further trained with the very same pipeline.config, or does it need to have a different fine tuning type? EDIT to make the question clearer, one can take as reference the ckpts mentioned in https://github.com/tensorflow/models/blob

问题 I have a CNN model built in keras which uses an SVM in its last layer. I get the prediction of this SVM by putting in an input into the CNN model, extracting the relevant features and then putting those features into my SVM to get an output prediction. This entire process I have names predict_DNR_tensor in the code below. This works fine and I am able to get a correct prediction. I am now trying to get a gradient of squared hinge loss of this prediction from my SVM wrt to the original input,

问题 I am trying to train a Reinforcement Learning agent using TF-Agent TF-Agent DQN Tutorial. In my application, I have 1 action containing 9 possible discrete values (labeled from 0 to 8). Below is the output from env.action_spec() BoundedTensorSpec(shape=(), dtype=tf.int64, name='action', minimum=array(0, dtype=int64), maximum=array(8, dtype=int64)) I would like to get the probability vector contains all actions calculated by the trained policy, and do further processing in other application

问题 I am trying to train a Reinforcement Learning agent using TF-Agent TF-Agent DQN Tutorial. In my application, I have 1 action containing 9 possible discrete values (labeled from 0 to 8). Below is the output from env.action_spec() BoundedTensorSpec(shape=(), dtype=tf.int64, name='action', minimum=array(0, dtype=int64), maximum=array(8, dtype=int64)) I would like to get the probability vector contains all actions calculated by the trained policy, and do further processing in other application

问题 So far, I was using tf.contrib.predictor.from_saved_model to load a SavedModel ( tf.estimator model class). However, this function has unfortunately been removed in TensorFlow v2. So far, in TensorFlow v1, my coding was the following: predict_fn = predictor.from_saved_model(model_dir + '/' + model, signature_def_key='predict') prediction_feed_dict = dict() for key in predict_fn._feed_tensors.keys(): #forec_data is a DataFrame holding the data to be fed in for index in forec_data.index:

问题 So far, I was using tf.contrib.predictor.from_saved_model to load a SavedModel ( tf.estimator model class). However, this function has unfortunately been removed in TensorFlow v2. So far, in TensorFlow v1, my coding was the following: predict_fn = predictor.from_saved_model(model_dir + '/' + model, signature_def_key='predict') prediction_feed_dict = dict() for key in predict_fn._feed_tensors.keys(): #forec_data is a DataFrame holding the data to be fed in for index in forec_data.index:

问题 So far, I was using tf.contrib.predictor.from_saved_model to load a SavedModel ( tf.estimator model class). However, this function has unfortunately been removed in TensorFlow v2. So far, in TensorFlow v1, my coding was the following: predict_fn = predictor.from_saved_model(model_dir + '/' + model, signature_def_key='predict') prediction_feed_dict = dict() for key in predict_fn._feed_tensors.keys(): #forec_data is a DataFrame holding the data to be fed in for index in forec_data.index: