tensorflow2.0

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 Problem I was following a Tensorflow 2 tutorial on how to load images with pure Tensorflow, because it is supposed to be faster than with Keras. The tutorial ends before showing how to split the resulting dataset (~ tf.Dataset ) into a train and validation dataset. I checked the reference for tf.Dataset and it does not contain a split() method. I tried slicing it manually but tf.Dataset neither contains a size() nor a length() method, so I don't see how I could slice it myself. I can't use

问题 Problem I was following a Tensorflow 2 tutorial on how to load images with pure Tensorflow, because it is supposed to be faster than with Keras. The tutorial ends before showing how to split the resulting dataset (~ tf.Dataset ) into a train and validation dataset. I checked the reference for tf.Dataset and it does not contain a split() method. I tried slicing it manually but tf.Dataset neither contains a size() nor a length() method, so I don't see how I could slice it myself. I can't use

问题 Problem I was following a Tensorflow 2 tutorial on how to load images with pure Tensorflow, because it is supposed to be faster than with Keras. The tutorial ends before showing how to split the resulting dataset (~ tf.Dataset ) into a train and validation dataset. I checked the reference for tf.Dataset and it does not contain a split() method. I tried slicing it manually but tf.Dataset neither contains a size() nor a length() method, so I don't see how I could slice it myself. I can't use

问题 Problem I was following a Tensorflow 2 tutorial on how to load images with pure Tensorflow, because it is supposed to be faster than with Keras. The tutorial ends before showing how to split the resulting dataset (~ tf.Dataset ) into a train and validation dataset. I checked the reference for tf.Dataset and it does not contain a split() method. I tried slicing it manually but tf.Dataset neither contains a size() nor a length() method, so I don't see how I could slice it myself. I can't use

问题 I am currently trying to get a hold of the TF2.0 api, but as I compared the GradientTape to a regular keras.Model.fit I noticed: It ran slower(probably due to the Eager Execution) It converged much slower (and I am not sure why). +--------+--------------+--------------+------------------+ | Epoch | GradientTape | GradientTape | keras.Model.fit | | | | shuffling | | +--------+--------------+--------------+------------------+ | 1 | 0.905 | 0.918 | 0.8793 | +--------+--------------+-------------

问题 I had lost my dataset by a careless mistake. I have only my tflite file left in my hand. Is there any solution to reverse back h5 file. I have been done decent research in this but no solutions found. 回答1: The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and