tensorflow2.0

问题 To make the results reproducible I've red more than 20 articles and added to my script maximum of the functions ... but failed. In the official source I red there are 2 kinds of seeds - global and operational. May be, the key to solving my problem is setting the operational seed, but I don't understand where to apply it. Would you, please, help me to achieve reproducible results with tensorflow (version > 2.0)? Thank you very much. from keras.models import Sequential from keras.layers import

问题 To make the results reproducible I've red more than 20 articles and added to my script maximum of the functions ... but failed. In the official source I red there are 2 kinds of seeds - global and operational. May be, the key to solving my problem is setting the operational seed, but I don't understand where to apply it. Would you, please, help me to achieve reproducible results with tensorflow (version > 2.0)? Thank you very much. from keras.models import Sequential from keras.layers import

问题 I am getting the following exception TypeError: An op outside of the function building code is being passed a "Graph" tensor. It is possible to have Graph tensors leak out of the function building context by including a tf.init_scope in your function building code. For example, the following function will fail: @tf.function def has_init_scope(): my_constant = tf.constant(1.) with tf.init_scope(): added = my_constant * 2 The graph tensor has name: conv2d_flipout/divergence_kernel:0 which also

问题 I am reimplementing a text2speech project. I am facing a Function call stack : keras_scratch_graph error in decoder part. The network architecture is from Deep Voice 3 paper. I am using keras from TF 2.0 on Google Colab. Below is the code for Decoder Keras Model. y1 = tf.ones(shape = (16, 203, 320)) def Decoder(name = "decoder"): # Decoder Prenet din = tf.concat((tf.zeros_like(y1[:, :1, -hp.mel:]), y1[:, :-1, -hp.mel:]), 1) keys = K.Input(shape = (180, 256), batch_size = 16, name = "keys")

问题 I am reimplementing a text2speech project. I am facing a Function call stack : keras_scratch_graph error in decoder part. The network architecture is from Deep Voice 3 paper. I am using keras from TF 2.0 on Google Colab. Below is the code for Decoder Keras Model. y1 = tf.ones(shape = (16, 203, 320)) def Decoder(name = "decoder"): # Decoder Prenet din = tf.concat((tf.zeros_like(y1[:, :1, -hp.mel:]), y1[:, :-1, -hp.mel:]), 1) keys = K.Input(shape = (180, 256), batch_size = 16, name = "keys")

问题 I'm trying to create a dataset that will return random windows from a time series, along with the next value as the target, using TensorFlow 2.0. I'm using Dataset.window() , which looks promising: import tensorflow as tf dataset = tf.data.Dataset.from_tensor_slices(tf.range(10)) dataset = dataset.window(5, shift=1, drop_remainder=True) for window in dataset: print([elem.numpy() for elem in window]) Outputs: [0, 1, 2, 3, 4] [1, 2, 3, 4, 5] [2, 3, 4, 5, 6] [3, 4, 5, 6, 7] [4, 5, 6, 7, 8] [5, 6

问题 I'm trying to create a dataset that will return random windows from a time series, along with the next value as the target, using TensorFlow 2.0. I'm using Dataset.window() , which looks promising: import tensorflow as tf dataset = tf.data.Dataset.from_tensor_slices(tf.range(10)) dataset = dataset.window(5, shift=1, drop_remainder=True) for window in dataset: print([elem.numpy() for elem in window]) Outputs: [0, 1, 2, 3, 4] [1, 2, 3, 4, 5] [2, 3, 4, 5, 6] [3, 4, 5, 6, 7] [4, 5, 6, 7, 8] [5, 6

问题 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv1d (Conv1D) (None, 35, 32) 96 _________________________________________________________________ batch_normalization (BatchNo (None, 35, 32) 128 _________________________________________________________________ dropout (Dropout) (None, 35, 32) 0 ______________________________________________________________

问题 Ubuntu - 20.04, Tensorflow - 2.2.0, Tensorboard - 2.2.1 I have read that one needs to reimplement the config method in order for a custom layer to be serializable. I have a custom layer that accepts arguments in its __init__ . It uses another custom layer and that consumes arguments in its __init__ as well. I can: Without Tensorboard callbacks: Use them in a model both in eager model and graph form Run tf.saved_model.save and it executes without a glich Load the thus saved model using tf

问题 I have a TF dataset to classify cats and dogs: import tensorflow_datasets as tfds SPLIT_WEIGHTS = (8, 1, 1) splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS) (raw_train, raw_validation, raw_test), metadata = tfds.load( 'cats_vs_dogs', split=list(splits), with_info=True, as_supervised=True) In the example they use some image augmentation with a map function. I was wondering if that could also be done with the nice ImageDataGenerator class such as described here: from tensorflow.keras