add gpu rnn units

Author: Lazy Programmer

nlp_class3/attention.py

@@ -16,6 +16,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 # make sure we do softmax over the time axis
 # expected shape is N x T x D

nlp_class3/bilstm_mnist.py

@@ -13,6 +13,10 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 def get_mnist(limit=None):
   if not os.path.exists('../large_files'):

nlp_class3/bilstm_test.py

@@ -9,6 +9,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+import keras.backend as K
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 T = 8
 D = 2

nlp_class3/lstm_toxic.py

@@ -19,6 +19,11 @@
 from keras.optimizers import Adam
 from sklearn.metrics import roc_auc_score
 
+import keras.backend as K
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 # Download the data:
 # https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge

nlp_class3/poetry.py

@@ -17,6 +17,11 @@
 from keras.preprocessing.sequence import pad_sequences
 from keras.optimizers import Adam, SGD
 
+import keras.backend as K
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 # some configuration
 MAX_SEQUENCE_LENGTH = 100

nlp_class3/simple_rnn_test.py

@@ -9,6 +9,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+import keras.backend as K
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 T = 8
 D = 2

nlp_class3/wseq2seq.py

@@ -15,6 +15,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+import keras.backend as K
+if len(K.tensorflow_backend._get_available_gpus()) > 0:
+  from keras.layers import CuDNNLSTM as LSTM
+  from keras.layers import CuDNNGRU as GRU
+
 
 # some config
 BATCH_SIZE = 64  # Batch size for training.