tensorflow

Is dropout layer still active in a freezed Keras model (i.e. trainable=False)?

由 |▌冷眼眸甩不掉的悲伤 提交于 2021-02-16 21:27:25
问题 I have two trained models ( model_A and model_B ), and both of them have dropout layers. I have freezed model_A and model_B and merged them with a new dense layer to get model_AB (but I have not removed model_A 's and model_B 's dropout layers). model_AB 's weights will be non-trainable, except for the added dense layer. Now my question is: are the dropout layers in model_A and model_B active (i.e. drop neurons) when I am training model_AB ? 回答1: Short answer: The dropout layers will continue

Is dropout layer still active in a freezed Keras model (i.e. trainable=False)?

由 大兔子大兔子 提交于 2021-02-16 21:22:49
问题 I have two trained models ( model_A and model_B ), and both of them have dropout layers. I have freezed model_A and model_B and merged them with a new dense layer to get model_AB (but I have not removed model_A 's and model_B 's dropout layers). model_AB 's weights will be non-trainable, except for the added dense layer. Now my question is: are the dropout layers in model_A and model_B active (i.e. drop neurons) when I am training model_AB ? 回答1: Short answer: The dropout layers will continue

Setting targetSdkVersion and compileSdkVersion for bazel, android tensorflow

由 让人想犯罪 __ 提交于 2021-02-16 21:07:21
问题 I need help in how to set the target and compile Sdk versions separately for bazel. I want to run tensorflow on the phone following this tutorial. After editing WORKSPACE for bazel as follows: android_sdk_repository( name = "androidsdk", api_level = 24, build_tools_version = "24.0.1", path = "/home/Android/Sdk", ) android_ndk_repository( name="androidndk", path="/home/Android/Sdk/ndk-bundle", api_level=21) I get PARSE ERROR on the phone upon apk installing: adb install -r bazel-bin/tensorflow

Setting targetSdkVersion and compileSdkVersion for bazel, android tensorflow

由 痴心易碎 提交于 2021-02-16 21:06:59
问题 I need help in how to set the target and compile Sdk versions separately for bazel. I want to run tensorflow on the phone following this tutorial. After editing WORKSPACE for bazel as follows: android_sdk_repository( name = "androidsdk", api_level = 24, build_tools_version = "24.0.1", path = "/home/Android/Sdk", ) android_ndk_repository( name="androidndk", path="/home/Android/Sdk/ndk-bundle", api_level=21) I get PARSE ERROR on the phone upon apk installing: adb install -r bazel-bin/tensorflow

Setting targetSdkVersion and compileSdkVersion for bazel, android tensorflow

由 99封情书 提交于 2021-02-16 21:06:54
问题 I need help in how to set the target and compile Sdk versions separately for bazel. I want to run tensorflow on the phone following this tutorial. After editing WORKSPACE for bazel as follows: android_sdk_repository( name = "androidsdk", api_level = 24, build_tools_version = "24.0.1", path = "/home/Android/Sdk", ) android_ndk_repository( name="androidndk", path="/home/Android/Sdk/ndk-bundle", api_level=21) I get PARSE ERROR on the phone upon apk installing: adb install -r bazel-bin/tensorflow

Setting targetSdkVersion and compileSdkVersion for bazel, android tensorflow

由 泄露秘密 提交于 2021-02-16 21:04:38
问题 I need help in how to set the target and compile Sdk versions separately for bazel. I want to run tensorflow on the phone following this tutorial. After editing WORKSPACE for bazel as follows: android_sdk_repository( name = "androidsdk", api_level = 24, build_tools_version = "24.0.1", path = "/home/Android/Sdk", ) android_ndk_repository( name="androidndk", path="/home/Android/Sdk/ndk-bundle", api_level=21) I get PARSE ERROR on the phone upon apk installing: adb install -r bazel-bin/tensorflow

Tensorflow installing error: __ is not a supported wheel on this platform

由 点点圈 提交于 2021-02-16 20:14:49
问题 I'm trying to install tensorflow on my PC but I keep getting errors. I have seen multiple posts about tensorflow installing errors online but all I found was solutions saying that the version of python was not compatible. However, I am using python 3.8 and I am using the URL for python 3.8 provided on tensorflow's website, so I don't see how that could be the issue. The command I'm using: python -m pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.2

Tensorflow：TensorFlow基础（一）

由 别等时光非礼了梦想. 提交于 2021-02-16 19:55:46
文章目录 TensorFlow基础 1.数据类型 1.1 数值类型 1.2 字符串类型 1.3 布尔类型 2.数值精度 2.1 读取精度 2.2 类型转换 3.待优化张量 4.创建张量 4.1 从数组、列表对象创建 4.2 创建全0或全1张量 4.3 创建自定义数值张量 4.4 创建已知分布的张量 4.4 创建序列 TensorFlow基础 import numpy as np import tensorflow as tf import tensorflow . keras as keras import tensorflow . keras . layers as layers physical_devices = tf . config . experimental . list_physical_devices ( 'GPU' ) assert len ( physical_devices ) > 0 , "Not enough GPU hardware devices available" tf . config . experimental . set_memory_growth ( physical_devices [ 0 ] , True ) 1.数据类型 Tensorflow主要有3种数据类型：数值型，字符串型，布尔型 1.1 数值类型 标量(Scalar)

Proper usage of `tf.scatter_nd` in tensorflow-r1.2

由 ▼魔方 西西 提交于 2021-02-16 19:08:09
问题 Given indices with shape [batch_size, sequence_len] , updates with shape [batch_size, sequence_len, sampled_size] , to_shape with shape [batch_size, sequence_len, vocab_size] , where vocab_size >> sampled_size , I'd like to use tf.scatter to map the updates to a huge tensor with to_shape , such that to_shape[bs, indices[bs, sz]] = updates[bs, sz] . That is, I'd like to map the updates to to_shape row by row. Please note that sequence_len and sampled_size are scalar tensors, while others are

Proper usage of `tf.scatter_nd` in tensorflow-r1.2

由 ╄→гoц情女王★ 提交于 2021-02-16 19:07:29
问题 Given indices with shape [batch_size, sequence_len] , updates with shape [batch_size, sequence_len, sampled_size] , to_shape with shape [batch_size, sequence_len, vocab_size] , where vocab_size >> sampled_size , I'd like to use tf.scatter to map the updates to a huge tensor with to_shape , such that to_shape[bs, indices[bs, sz]] = updates[bs, sz] . That is, I'd like to map the updates to to_shape row by row. Please note that sequence_len and sampled_size are scalar tensors, while others are
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