tflearn暂未适配Windows版TensorFlow 0.12，TensorFlow-GPU 1.5运行报错求助

Hey there, let's work through your issues step by step given your setup: TensorFlow-GPU 1.5, Python 3.5, TFLearn 0.3.2, and Anaconda 4.4.9.

That note about TFLearn not being ported to Windows TF 0.12 is outdated for your TF 1.5 setup—TFLearn 0.3.2 does support TF 1.x on Windows, but there's a patched version that fixes some Windows-specific bugs. Here's what to do first:

• Upgrade to the patched TFLearn release: Uninstall your current version and install the post-release build tailored for TF 1.x compatibility. Run these commands in your Anaconda prompt:

  pip uninstall tflearn -y
  pip install tflearn==0.3.2.post1
  

• Update sample code for TF 1.5 syntax: If the sample code was written for older TF versions (like 0.12), you'll need to adjust deprecated API calls:
  • Add parentheses to tf.contrib.layers.xavier_initializer (change to tf.contrib.layers.xavier_initializer()) if you see initialization errors.
  • Use tf.Session() instead of the deprecated tf.InteractiveSession() if the code relies on old session management.
  • Double-check tf.placeholder dtype and shape definitions to match your input data—TF 1.5 still uses placeholders, but mismatches can cause silent failures or errors.

Environment conflicts or GPU misconfiguration often cause unexpected errors. Let's confirm your setup is solid:

• Always activate your Anaconda environment first: Before running code, make sure you're in the environment where TF-GPU 1.5 is installed:

  activate your_environment_name
  

• Check GPU compatibility: TensorFlow 1.5 requires CUDA 9.0 and cuDNN 7.0. Verify your GPU is detected by running this snippet:

  import tensorflow as tf
  print(tf.test.is_gpu_available())
  

  If this returns False, double-check that your CUDA and cuDNN installation paths are added to your system's PATH environment variable.

If you're still hitting errors, zero in on the exact error line and message:

• If you see AttributeError: module 'tensorflow' has no attribute 'xxx', that's a clear sign the code uses a deprecated TF API. Look up the TF 1.5 equivalent in the TensorFlow 1.5 documentation archive.
• For TFLearn import errors, ensure you're not mixing pip and conda installs—stick to one package manager for your environment to avoid dependency conflicts.

内容的提问来源于stack exchange，提问作者IVRafa