tensorflow

问题 Even after setting tf.config.threading.set_inter_op_parallelism_threads(1) and tf.config.threading.set_intra_op_parallelism_threads(1) Keras with Tensorflow CPU (running a simple CNN model fit) on a linux machine is creating too many threads. Whatever I try it seems to be creating 94 threads while going through the fitting epochs. Have tried playing with tf.compat.v1.ConfigProto settings but nothing helps. How do I limit the number of threads? 回答1: This is why tensorflow created many threads.

问题 I just completed ANN course and started learning CNN. I have basic understanding of padding and stride operation works in CNN. But have difficultly in mapping input image with neurons in first conv layer but i have basic understanding of how input features are mapped to first hidden layer in ANN. What is best way of understanding mapping between input image with neurons in first conv layer? How can I clarify my doubts about the below code example? Code is taken from DL course in Coursera. def

问题 Even after setting tf.config.threading.set_inter_op_parallelism_threads(1) and tf.config.threading.set_intra_op_parallelism_threads(1) Keras with Tensorflow CPU (running a simple CNN model fit) on a linux machine is creating too many threads. Whatever I try it seems to be creating 94 threads while going through the fitting epochs. Have tried playing with tf.compat.v1.ConfigProto settings but nothing helps. How do I limit the number of threads? 回答1: This is why tensorflow created many threads.

问题 I just completed ANN course and started learning CNN. I have basic understanding of padding and stride operation works in CNN. But have difficultly in mapping input image with neurons in first conv layer but i have basic understanding of how input features are mapped to first hidden layer in ANN. What is best way of understanding mapping between input image with neurons in first conv layer? How can I clarify my doubts about the below code example? Code is taken from DL course in Coursera. def

问题 I would like to be able to reset the weights of my entire Keras model so that I do not have to compile it again. Compiling the model is currently the main bottleneck of my code. Here is an example of what I mean: import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(10) ]) model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001), loss=tf.keras.losses

问题 It is recommended to use tensorflow dataset as the input pipeline which can be set up as follows: # Specify dataset dataset = tf.data.Dataset.from_tensor_slices((features, labels)) # Suffle dataset = dataset.shuffle(buffer_size=1e5) # Specify batch size dataset = dataset.batch(128) # Create an iterator iterator = dataset.make_one_shot_iterator() # Get next batch next_batch = iterator.get_next() I should be able to get the batch size (either from dataset itself or from an iterator created from

问题 It is recommended to use tensorflow dataset as the input pipeline which can be set up as follows: # Specify dataset dataset = tf.data.Dataset.from_tensor_slices((features, labels)) # Suffle dataset = dataset.shuffle(buffer_size=1e5) # Specify batch size dataset = dataset.batch(128) # Create an iterator iterator = dataset.make_one_shot_iterator() # Get next batch next_batch = iterator.get_next() I should be able to get the batch size (either from dataset itself or from an iterator created from

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: