Cleaned a little bit of code

mini_proj/waldo_model.py

@@ -25,7 +25,7 @@ from keras.utils import to_categorical
 '''
 Model definition define the network structure
 '''
-def FCN():
+def CNN():
     ## List of model layers
     inputs = Input((3, 64, 64))
 
@@ -33,7 +33,6 @@ def FCN():
     m_pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
 
     conv2 = Conv2D(32, (3, 3), activation='relu', padding='same')(m_pool1)
-    #drop1 = Dropout(0.2)(conv2) # Drop some portion of features to prevent overfitting
     m_pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
 
     conv3 = Conv2D(32, (3, 3), activation='relu', padding='same')(m_pool2)
@@ -47,13 +46,81 @@ def FCN():
     drop3 = Dropout(0.2)(dense)
     classif = Dense(2, activation='sigmoid')(drop3)    # Final layer to classify
 
-    ## Define the model structure
+    ## Define the model start and end
     model = Model(inputs=inputs, outputs=classif)
     # Optimizer recommended Adadelta values (lr=0.01)
     model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy', f1])  
 
     return model
 
+'''
+Model definition for a fully convolutional (no dense layers) network structure
+'''
+def FCN():
+    ## List of model layers
+    inputs = Input((3, 64, 64))
+
+    conv1 = Conv2D(16, (3, 3), activation='relu', padding='same', input_shape=(64, 64, 3))(inputs)
+    m_pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+
+    conv2 = Conv2D(32, (3, 3), activation='relu', padding='same')(m_pool1)
+    m_pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
+
+    conv3 = Conv2D(32, (3, 3), activation='relu', padding='same')(m_pool2)
+    drop2 = Dropout(0.2)(conv3) # Drop some portion of features to prevent overfitting
+    m_pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)
+
+    conv4 = Conv2D(64, (2, 2), activation='relu', padding='same')(m_pool2)
+
+    flat = Flatten()(conv4)                             # Makes data 1D
+    drop3 = Dropout(0.2)(flat)
+    classif = Dense(2, activation='sigmoid')(drop3)    # Final layer to classify
+
+    ## Define the model start and end
+    model = Model(inputs=inputs, outputs=classif)
+    # Optimizer recommended Adadelta values (lr=0.01)
+    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy', f1])  
+
+    return model
+
+
+'''
+Model definition for the network structure of LeNet
+Note: LeNet was designed to classify into 10 classes, but we are only performing binary classification
+'''
+def LeNet():
+    ## List of model layers
+    inputs = Input((3, 64, 64))
+
+    conv1 = Conv2D(6, (5, 5), activation='relu', padding='valid', input_shape=(64, 64, 3))(inputs)
+    m_pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+
+    conv2 = Conv2D(16, (5, 5), activation='relu', padding='valid')(m_pool1)
+    m_pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
+
+    flat = Flatten()(m_pool2)                               # Makes data 1D
+    
+    dense1 = Dense(120, activation='relu')(flat)          # Fully connected layer
+    dense2 = Dense(84, activation='relu')(dense1)         # Fully connected layer
+    drop3 = Dropout(0.2)(dense2)
+    classif = Dense(2, activation='sigmoid')(drop3)    # Final layer to classify
+
+    ## Define the model start and end
+    model = Model(inputs=inputs, outputs=classif)
+    model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy', f1])  
+
+    return model
+
+'''
+AlexNet architecture
+'''
+def AlexNet():
+    inputs = Input(shape=(3, 64, 64))
+
+    
+    return model
+
+
 def f1(y_true, y_pred):
     def recall(y_true, y_pred):
         """Recall metric.
@@ -110,14 +177,16 @@ lbl_train = to_categorical(lbl_train)       # One hot encoding the labels
 lbl_test = to_categorical(lbl_test)
 
 ## Define model
+#model = CNN()
 model = FCN()
+#model = LeNet()
 # svm_iclf = ImageClassifier(svm.SVC)
 # tree_iclf = ImageClassifier(tree.DecisionTreeClassifier)
 # naive_bayes_iclf = ImageClassifier(naive_bayes.GaussianNBd)
 # ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
 
 ## Define training parameters
-epochs = 10             # an epoch is one forward pass and back propogation of all training data
+epochs = 25             # an epoch is one forward pass and back propogation of all training data
 batch_size = 150        # batch size - number of training example used in one forward/backward pass
 # (higher batch size uses more memory, smaller batch size takes more time)
 #lrate = 0.01           # Learning rate of the model - controls magnitude of weight changes in training the NN