tensorflow2.0

问题 I'm trying to implement smart reply https://www.tensorflow.org/lite/models/smart_reply/overview concept in Python. You can download tflite model file here https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip. import numpy as np import tensorflow as tf interpreter = tf.lite.Interpreter(model_path="smartreply.tflite") interpreter.allocate_tensors() While using above code, i'm getting this error, Traceback (most recent call last): File "smart_reply

问题 The CuDNNGRU in TensorFlow 1.0 is really fast. But when I shifted to TensorFlow 2.0 i am unable to find CuDNNGRU . Simple GRU is really slow in TensorFlow 2.0 . Is there any way to use CuDNNGRU in TensorFlow 2.0 ? 回答1: The importable implementations have been deprecated - instead, LSTM and GRU will default to CuDNNLSTM and CuDNNGRU if all conditions are met: activation = 'tanh' recurrent_activation = 'sigmoid' recurrent_dropout = 0 unroll = False use_bias = True Inputs, if masked , are

问题 The CuDNNGRU in TensorFlow 1.0 is really fast. But when I shifted to TensorFlow 2.0 i am unable to find CuDNNGRU . Simple GRU is really slow in TensorFlow 2.0 . Is there any way to use CuDNNGRU in TensorFlow 2.0 ? 回答1: The importable implementations have been deprecated - instead, LSTM and GRU will default to CuDNNLSTM and CuDNNGRU if all conditions are met: activation = 'tanh' recurrent_activation = 'sigmoid' recurrent_dropout = 0 unroll = False use_bias = True Inputs, if masked , are

问题 I'm trying to use gradient_override_map with Tensorflow 2.0. There is an example in the documentation, which I will use as the example here as well. In 2.0, GradientTape can be used to compute gradients as follows: import tensorflow as tf print(tf.version.VERSION) # 2.0.0-alpha0 x = tf.Variable(5.0) with tf.GradientTape() as tape: s_1 = tf.square(x) print(tape.gradient(s_1, x)) There is also the tf.custom_gradient decorator, which can be used to define the gradient for a new function (again,

问题 I'm trying to use gradient_override_map with Tensorflow 2.0. There is an example in the documentation, which I will use as the example here as well. In 2.0, GradientTape can be used to compute gradients as follows: import tensorflow as tf print(tf.version.VERSION) # 2.0.0-alpha0 x = tf.Variable(5.0) with tf.GradientTape() as tape: s_1 = tf.square(x) print(tape.gradient(s_1, x)) There is also the tf.custom_gradient decorator, which can be used to define the gradient for a new function (again,

问题 I'm trying to use gradient_override_map with Tensorflow 2.0. There is an example in the documentation, which I will use as the example here as well. In 2.0, GradientTape can be used to compute gradients as follows: import tensorflow as tf print(tf.version.VERSION) # 2.0.0-alpha0 x = tf.Variable(5.0) with tf.GradientTape() as tape: s_1 = tf.square(x) print(tape.gradient(s_1, x)) There is also the tf.custom_gradient decorator, which can be used to define the gradient for a new function (again,

问题 An official tutorial on @tf.function says: To get peak performance and to make your model deployable anywhere, use tf.function to make graphs out of your programs. Thanks to AutoGraph, a surprising amount of Python code just works with tf.function, but there are still pitfalls to be wary of. The main takeaways and recommendations are: Don't rely on Python side effects like object mutation or list appends. tf.function works best with TensorFlow ops, rather than NumPy ops or Python primitives.

问题 An official tutorial on @tf.function says: To get peak performance and to make your model deployable anywhere, use tf.function to make graphs out of your programs. Thanks to AutoGraph, a surprising amount of Python code just works with tf.function, but there are still pitfalls to be wary of. The main takeaways and recommendations are: Don't rely on Python side effects like object mutation or list appends. tf.function works best with TensorFlow ops, rather than NumPy ops or Python primitives.

问题 An official tutorial on @tf.function says: To get peak performance and to make your model deployable anywhere, use tf.function to make graphs out of your programs. Thanks to AutoGraph, a surprising amount of Python code just works with tf.function, but there are still pitfalls to be wary of. The main takeaways and recommendations are: Don't rely on Python side effects like object mutation or list appends. tf.function works best with TensorFlow ops, rather than NumPy ops or Python primitives.

问题 I have a large dataset that can fit in host memory. However, when I use tf.keras to train the model, it yields GPU out-of-memory problem. Then I look into tf.data.Dataset and want to use its batch() method to batch the training dataset so that it can execute the model.fit() in GPU. According to its documentation, an example is as follows: train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels)) test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test