tf.keras

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 am training a model in tf.keras with tensorflow 2.0. I am having an issue where my model appears to train successfully, but it is not iterating through the entire dataset. I restructured the code into tensorflow 1.15, and I do not have this issue in tensorflow 1.x. I am following this tutorial for Multiple Input Series. Below are more details: I have a time-series dataset. It is very small so I am able to load it into memory, so I do not need the dataset API. I am windowing the time-series

问题 I just started working with Keras and noticed that there are two layers with very similar names for max pooling: MaxPool and MaxPooling . I was surprised that I couldn't find the difference between these two on Google; so I am wondering what the difference is between the two if any. 回答1: they are the same. you can test it by your own import numpy as np import tensorflow as tf from tensorflow.keras.layers import * # create dummy data X = np.random.uniform(0,1, (32,5,3)).astype(np.float32)

问题 I just started working with Keras and noticed that there are two layers with very similar names for max pooling: MaxPool and MaxPooling . I was surprised that I couldn't find the difference between these two on Google; so I am wondering what the difference is between the two if any. 回答1: they are the same. you can test it by your own import numpy as np import tensorflow as tf from tensorflow.keras.layers import * # create dummy data X = np.random.uniform(0,1, (32,5,3)).astype(np.float32)

问题 I am trying to implement a recent paper. Part of this implementation involves moving from tf 1.14 to tf 2.1.0. The code was working with tf 1.14 but is no longer working. NOTE: If I disable eager execution tf.compat.v1.disable_eager_execution() then the code works as expected. Is this the solution? I've made plenty of models before in TF 2.x and never had to disable eager execution to achieve normal functionality. I have distilled the problem to a very short gist that shows what's happening.

问题 I have a code running Keras with TensorFlow 1. The code modifies the loss function in order to do deep reinforcement learning: import os import gym import numpy as np import pandas as pd import matplotlib.pyplot as plt env = gym.make("CartPole-v0").env env.reset() n_actions = env.action_space.n state_dim = env.observation_space.shape from tensorflow import keras import random from tensorflow.keras import layers as L import tensorflow as tf from tensorflow.python.keras.backend import set

问题 I have a code running Keras with TensorFlow 1. The code modifies the loss function in order to do deep reinforcement learning: import os import gym import numpy as np import pandas as pd import matplotlib.pyplot as plt env = gym.make("CartPole-v0").env env.reset() n_actions = env.action_space.n state_dim = env.observation_space.shape from tensorflow import keras import random from tensorflow.keras import layers as L import tensorflow as tf from tensorflow.python.keras.backend import set

问题 I have a code running Keras with TensorFlow 1. The code modifies the loss function in order to do deep reinforcement learning: import os import gym import numpy as np import pandas as pd import matplotlib.pyplot as plt env = gym.make("CartPole-v0").env env.reset() n_actions = env.action_space.n state_dim = env.observation_space.shape from tensorflow import keras import random from tensorflow.keras import layers as L import tensorflow as tf from tensorflow.python.keras.backend import set

问题 I am following this tutorial on creating a custom Model using TensorFlow lite Model Maker on Collab. import pathlib path = pathlib.Path('/content/employee_pics') count = len(list(path.glob('*/*.jpg'))) count data = ImageClassifierDataLoader.from_folder(path) train_data, test_data = data.split(0.5) I have an issue with step 2: model = image_classifier.create(train_data) I get an error: ValueError: Expect x to be a non-empty array or dataset. Am I doing something wrong? The data set provided in