Merge pull request #91 from medicode/revert-90-master

Revert "update my branch further"

Author: cbockman

.travis.yml

@@ -42,7 +42,7 @@ install:
   - pip install -q .
   - t2t-trainer --registry_help
   # Then install the test dependencies
-  - pip install -q .[tests,allen]
+  - pip install -q .[tests]
   # Make sure to install the atari extras for gym
   - pip install "gym[atari]"
   # Make sure we have the latest version of numpy - avoid problems we were
@@ -59,7 +59,6 @@ script:
   #   * trainer_lib_test
   #   * visualization_test
   #   * model_rl_experiment_test
-  #   * allen_brain_test
   #   * model_rl_experiment_stochastic_test
   #   * models/research
   # algorithmic_math_test: flaky
@@ -72,13 +71,11 @@ script:
     --ignore=tensor2tensor/data_generators/algorithmic_math_test.py
     --ignore=tensor2tensor/models/research/universal_transformer_test.py
     --ignore=tensor2tensor/rl/model_rl_experiment_test.py
-    --ignore=tensor2tensor/data_generators/allen_brain_test.py
     --ignore=tensor2tensor/rl/model_rl_experiment_stochastic_test.py
     --ignore=tensor2tensor/models/research
   - pytest tensor2tensor/utils/registry_test.py
   - pytest tensor2tensor/utils/trainer_lib_test.py
   - pytest tensor2tensor/visualization/visualization_test.py
-  - pytest tensor2tensor/data_generators/allen_brain_test.py
   - if [[ "$TF_VERSION" == "$TF_LATEST"  ]] || [[ "$TF_VERSION" == "tf-nightly"  ]];
     then
       pytest tensor2tensor/models/research;

README.md

@@ -113,13 +113,9 @@ For (un)conditional image generation, we have a number of standard data-sets:
     32x32 or `--problem=image_imagenet64_gen_rev` for 64x64.
 
 We suggest to use the Image Transformer, i.e., `--model=imagetransformer`, or
-the Image Transformer Plus, i.e., `--model=imagetransformerpp` that uses
-discretized mixture of logistics, or variational auto-encoder, i.e.,
-`--model=transformer_ae`.
-For CIFAR-10, using `--hparams_set=imagetransformer_cifar10_base` or
-`--hparams_set=imagetransformer_cifar10_base_dmol` yields 2.90 bits per
-dimension. For Imagenet-32, using
-`--hparams_set=imagetransformer_imagenet32_base` yields 3.77 bits per dimension.
+variational auto-encoder, i.e., `--model=transformer_ae`. For CIFAR-10, using
+`--hparams_set=imagetransformerpp_base_12l_8h_big_uncond_dr03_dan_m` yields 2.90
+bits per dimension.
 
 ### Language Modeling
 

docs/walkthrough.md

@@ -113,13 +113,9 @@ For (un)conditional image generation, we have a number of standard data-sets:
     32x32 or `--problem=image_imagenet64_gen_rev` for 64x64.
 
 We suggest to use the Image Transformer, i.e., `--model=imagetransformer`, or
-the Image Transformer Plus, i.e., `--model=imagetransformerpp` that uses
-discretized mixture of logistics, or variational auto-encoder, i.e.,
-`--model=transformer_ae`.
-For CIFAR-10, using `--hparams_set=imagetransformer_cifar10_base` or
-`--hparams_set=imagetransformer_cifar10_base_dmol` yields 2.90 bits per
-dimension. For Imagenet-32, using
-`--hparams_set=imagetransformer_imagenet32_base` yields 3.77 bits per dimension.
+variational auto-encoder, i.e., `--model=transformer_ae`. For CIFAR-10, using
+`--hparams_set=imagetransformerpp_base_12l_8h_big_uncond_dr03_dan_m` yields 2.90
+bits per dimension.
 
 ### Language Modeling
 

setup.py

@@ -58,7 +58,6 @@
             # explicit pip install gym[atari] for the tests.
             # 'gym[atari]',
         ],
-        'allen': ['Pillow==5.1.0', 'pandas==0.23.0'],
     },
     classifiers=[
         'Development Status :: 4 - Beta',

tensor2tensor/data_generators/all_problems.py

@@ -23,7 +23,6 @@
 MODULES = [
     "tensor2tensor.data_generators.algorithmic",
     "tensor2tensor.data_generators.algorithmic_math",
-    "tensor2tensor.data_generators.allen_brain",
     "tensor2tensor.data_generators.audio",
     "tensor2tensor.data_generators.babi_qa",
     "tensor2tensor.data_generators.bair_robot_pushing",