Add small FC

keras/main.py

@@ -20,6 +20,7 @@ from models.lstm64x2_embed2_10dense_shared import LSTM64x2_Embed2_10Dense_S
 from models.lstm64x2_embed4_10dense_shared import LSTM64x2_Embed4_10Dense_S
 from models.fc6_embed3_2dense import FC6_Embed3_2Dense
 from models.fc2_2dense import FC2_2Dense
+from models.fc2-100_2dense import FC2_100_2Dense
 
 import tensorflow as tf
 from keras import callbacks
@@ -90,6 +91,8 @@ def factory_model( model_name ):
         return FC6_Embed3_2Dense(), 'fc6_embed3_2dense'
     elif model_name == 'fc2_2dense':
         return FC2_2Dense(), 'fc2_2dense'
+    elif model_name == 'fc2_100_2dense':
+        return FC2_100_2Dense(), 'fc2_100_2dense'
     else:
         print("Model unknown. Terminating.")
         sys.exit(1)
@@ -121,7 +124,7 @@ def make_parser():
     parser.add_argument('-train', type=str, help='File containing the training set')
     parser.add_argument('-val', type=str, help='File containing the validation set')
     parser.add_argument('-test', type=str, help='File containing the test set')
-    parser.add_argument('-model', type=str, help='choose among: lstm32_3conv3_2dense, lstm32_3conv3_2dense_shared, lstm32_3conv4_2dense_shared, lstm64_3conv3_2dense_shared, lstm64drop_3conv3_3dense_shared, lstm64x2_3conv3_10dense_shared, lstm64x2_embed2_10dense_shared, lstm64x2_embed4_10dense_shared, fc6_embed3_2dense, fc2_2dense')
+    parser.add_argument('-model', type=str, help='choose among: lstm32_3conv3_2dense, lstm32_3conv3_2dense_shared, lstm32_3conv4_2dense_shared, lstm64_3conv3_2dense_shared, lstm64drop_3conv3_3dense_shared, lstm64x2_3conv3_10dense_shared, lstm64x2_embed2_10dense_shared, lstm64x2_embed4_10dense_shared, fc6_embed3_2dense, fc2_2dense, fc2_100_2dense')
     parser.add_argument('-epochs', type=int, default=50, help='Number of epochs [default: 50]')
     parser.add_argument('-batch', type=int, default=64, help='Batch size [default: 64]')
     parser.add_argument('-patience', type=int, default=0, help='Number of epochs before triggering the early stopping criterion [default: infinite patience]')

keras/models/fc2-100_2dense.py

+from keras.models import Model
+from keras import layers
+from keras import Input
+import numpy as np
+import tensorflow as tf 
+
+from models.abstract_model import AbstractModel
+
+class FC2_100_2Dense(AbstractModel):
+    def __init__(self):
+        super().__init__()
+
+    def get_model(self):
+        input1 = Input(shape=(1166,20,), dtype=np.float32, name='protein1')
+        protein1 = layers.Flatten()(input1)
+        protein1 = layers.Dense(100, activation='relu')(protein1)
+        protein1 = layers.BatchNormalization()(protein1)
+        protein1 = layers.Dense(100, activation='relu')(protein1)
+        protein1 = layers.BatchNormalization()(protein1)
+        
+        input2 = Input(shape=(1166,20,), dtype=np.float32, name='protein2')
+        protein2 = layers.Flatten()(input2)
+        protein2 = layers.Dense(100, activation='relu')(protein2)
+        protein2 = layers.BatchNormalization()(protein2)
+        protein2 = layers.Dense(100, activation='relu')(protein2)
+        protein2 = layers.BatchNormalization()(protein2)
+        
+        head = layers.concatenate([protein1, protein2], axis=-1)
+        head = layers.Dense(100, activation='relu')(head)
+        head = layers.BatchNormalization()(head)
+        head = layers.Dense(1)(head)
+        output = layers.Activation(tf.nn.sigmoid)(head)
+        
+        model = Model(inputs=[input1, input2], outputs=output)
+        return model