model = ResNet50(input_shape=input_shape,
weights=None,
classes=num_classes)
elif runmodel == "MNET":
from keras.applications.mobilenet import MobileNet
model = MobileNet(input_shape=input_shape,
weights=None,
classes=num_classes)
elif runmodel == "CNN2":
model = keras.models.load_model("leaf.CNN.h5")
elif runmodel == "CNN":
# Create the model
model = Sequential()
model.add(
Conv2D(64, (3, 3),
input_shape=input_shape,
padding='same',
activation='relu',
kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Conv2D(128, (3, 3),
padding='same',
activation='relu',
kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Conv2D(256, (3, 3),
padding='same',
activation='relu',
kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
def DefineArchitecture(architectureName, classes, customImageSize):
if architectureName == "VGG16":
imageSize = 224
baseModel = VGG16(weights='imagenet', include_top=True)
elif architectureName == "VGG19":
imageSize = 224
baseModel = VGG19(weights='imagenet', include_top=True)
elif architectureName == "ResNet50":
imageSize = 224
baseModel = ResNet50(weights='imagenet', include_top=True)
elif architectureName == "InceptionV3":
imageSize = 299
baseModel = InceptionV3(weights='imagenet', include_top=True)
elif architectureName == "InceptionResNetV2":
imageSize = 299
baseModel = InceptionResNetV2(weights='imagenet', include_top=True)
elif architectureName == "MobileNet":
imageSize = 224
baseModel = MobileNet(weights='imagenet', include_top=True)
elif architectureName == "MobileNetV2":
imageSize = 224
baseModel = MobileNetV2(weights='imagenet', include_top=True)
elif architectureName == "DenseNet121":
imageSize = 224
baseModel = DenseNet121(weights='imagenet', include_top=True)
elif architectureName == "DenseNet169":
imageSize = 224
baseModel = DenseNet169(weights='imagenet', include_top=True)
elif architectureName == "DenseNet201":
imageSize = 224
baseModel = DenseNet201(weights='imagenet', include_top=True)
elif architectureName == "Custom Model (1-Layer Network w 32 Filters)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(32, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Flatten())
baseModel.add(Dense(128, activation='relu'))
baseModel.add(Dropout(0.5))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (2-Layer Network)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(32, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(32, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Flatten())
baseModel.add(Dense(128, activation='relu'))
baseModel.add(Dropout(0.5))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (1-Layer Network w 64 Filters)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(64, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Flatten())
baseModel.add(Dense(128, activation='relu'))
baseModel.add(Dropout(0.5))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (1-Layer Network w 128 Filters)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(customImageSize, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Flatten())
baseModel.add(Dense(128, activation='relu'))
baseModel.add(Dropout(0.5))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (3-Layers)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(32, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(32, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(64, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Flatten())
baseModel.add(Dense(128, activation='relu'))
baseModel.add(Dropout(0.5))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (CFCN-Shallow)":
imageSize = customImageSize
# Create CNN architecture
modelInput = Input(shape=(customImageSize, customImageSize, 3))
padding_1 = ZeroPadding2D(padding=1)(modelInput)
conv2D_1a = Conv2D(32, (5, 5), strides=(2, 2),
activation='relu')(padding_1)
conv2D_1b = Conv2D(32, (11, 11), strides=(4, 4),
activation='relu')(modelInput)
# conv2D_1c = Conv2D(32, (5,5), activation='relu')(modelInput)
conv2D_1 = Conv2D(32, (3, 3), activation='relu')(modelInput)
pool_1 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_1)
merged1 = keras.layers.concatenate([pool_1, conv2D_1a])
conv2D_2a = Conv2D(64, (5, 5), strides=(2, 2),
activation='relu')(merged1)
conv2D_2 = Conv2D(64, (3, 3), activation='relu')(merged1)
# pool_2a = MaxPooling2D((4, 4), strides=(4,4))(conv2D_1c)
pool_2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_2)
merged2 = keras.layers.concatenate([pool_2, conv2D_2a, conv2D_1b])
padding_2 = ZeroPadding2D(padding=1)(merged2)
conv2D_3a = Conv2D(128, (5, 5), strides=(2, 2),
activation='relu')(padding_2)
conv2D_3 = Conv2D(128, (3, 3), activation='relu')(merged2)
pool_3 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_3)
merged3 = keras.layers.concatenate([pool_3, conv2D_3a])
dense = Flatten()(merged3)
output = Dense(128, activation='relu')(dense)
dropout = Dropout(0.5)(output)
predictions = Dense(classes, activation='softmax')(dropout)
baseModel = Model(inputs=modelInput, outputs=predictions)
elif architectureName == "Custom Model (Linear-5 layers)":
imageSize = customImageSize
baseModel = Sequential()
baseModel.add(
Conv2D(128, (3, 3),
activation='relu',
input_shape=(customImageSize, customImageSize, 3)))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(64, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(32, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(32, (3, 3), activation='relu'))
baseModel.add(MaxPooling2D(pool_size=(2, 2)))
baseModel.add(Conv2D(32, (3, 3), activation='relu'))
baseModel.add(Flatten())
baseModel.add(Dropout(0.5))
baseModel.add(
Dense(128,
activation='relu',
kernel_regularizer=regularizers.l2(0.01)))
baseModel.add(Dropout(0.5))
baseModel.add(
Dense(64,
activation='relu',
kernel_regularizer=regularizers.l2(0.01)))
baseModel.add(Dense(classes, activation='softmax'))
elif architectureName == "Custom Model (CFNC-Deep)":
imageSize = customImageSize
# Create CNN architecture
modelInput = Input(shape=(customImageSize, customImageSize, 3))
padding_1 = ZeroPadding2D(padding=1)(modelInput)
conv2D_1a = Conv2D(128, (3, 3), activation='relu')(modelInput)
conv2d_1b = Conv2D(128, (5, 5), strides=(2, 2),
activation='relu')(padding_1)
conv2d_1c = Conv2D(128, (11, 11), strides=(4, 4),
activation='relu')(modelInput)
pool_1 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_1a)
merged_1 = keras.layers.concatenate([pool_1, conv2d_1b])
conv2D_2a = Conv2D(64, (3, 3), activation='relu')(merged_1)
conv2D_2b = Conv2D(64, (5, 5), strides=(2, 2),
activation='relu')(merged_1)
pool_2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_2a)
merged_2 = keras.layers.concatenate([pool_2, conv2D_2b, conv2d_1c])
pool_2a = MaxPooling2D((2, 2), strides=(2, 2))(merged_2)
padding_2 = ZeroPadding2D(padding=1)(merged_2)
conv2D_3a = Conv2D(32, (3, 3), activation='relu')(merged_2)
conv2D_3b = Conv2D(32, (5, 5), strides=(2, 2),
activation='relu')(padding_2)
conv2D_2c = Conv2D(64, (11, 11), strides=(4, 4),
activation='relu')(padding_2)
pool_3 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_3a)
merged_3 = keras.layers.concatenate([pool_3, conv2D_3b])
padding_3 = ZeroPadding2D(padding=1)(merged_3)
conv2D_4a = Conv2D(32, (3, 3), activation='relu')(merged_3)
conv2D_4b = Conv2D(32, (5, 5), strides=(2, 2),
activation='relu')(padding_3)
conv2D_4c = Conv2D(32, (5, 5), strides=(2, 2),
activation='relu')(pool_2a)
conv2D_4d = Conv2D(32, (7, 7), strides=(2, 2),
activation='relu')(merged_3)
pool_4 = MaxPooling2D((2, 2), strides=(2, 2))(conv2D_4a)
merged_4 = keras.layers.concatenate(
[pool_4, conv2D_4b, conv2D_2c, conv2D_4c])
conv2D_5 = Conv2D(32, (3, 3), activation='relu')(merged_4)
merged_5 = keras.layers.concatenate([conv2D_5, conv2D_4d])
flatten = Flatten()(merged_5)
dropout = Dropout(0.5)(flatten)
dense_1 = Dense(128, activation='relu')(dropout)
dropout = Dropout(0.5)(dense_1)
dense_1 = Dense(64, activation='relu')(dropout)
predictions = Dense(classes, activation='softmax')(dropout)
baseModel = Model(inputs=modelInput, outputs=predictions)
return imageSize, baseModel
