def CNN(nb_classes, img_dim, pretr_weights_file=None, model_name=None):
"""
Build Convolution Neural Network
args : nb_classes (int) number of classes
img_dim (tuple of int) num_chan, height, width
returns : model (keras NN) the Neural Net model
"""
model = Sequential()
model.add(Convolution2D(32, 3, 3, name="convolution2d_1", input_shape=(3, 224, 224), border_mode="same", activation='relu'))
model.add(Convolution2D(32, 3, 3, name="convolution2d_2", border_mode="same", activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_1"))
model.add(Convolution2D(64, 3, 3, name="convolution2d_3", border_mode="same", activation='relu'))
model.add(Convolution2D(64, 3, 3, name="convolution2d_4", border_mode="same", activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_2"))
model.add(Convolution2D(128, 3, 3, name="convolution2d_5", border_mode="same", activation='relu'))
model.add(Convolution2D(128, 3, 3, name="convolution2d_6", border_mode="same", activation='relu'))
model.add(Convolution2D(128, 3, 3, name="convolution2d_7", border_mode="same", activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2, 2), name="maxpooling2d_3"))
model.add(Flatten(name="flatten_1"))
model.add(Dense(1024, activation='relu', name="dense_1"))
model.add(Dropout(0.5, name="dropout_1"))
model.add(Dense(1024, activation='relu', name="dense_2"))
model.add(Dropout(0.5, name="dropout_2"))
model.add(Dense(nb_classes, activation='softmax', name="dense_3"))
if model_name:
model.name = model_name
else:
model.name = "CNN"
if pretr_weights_file:
model.load_weights(pretr_weights_file)
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
model.add(Dense(nb_classes, activation='softmax', name="dense_4"))
return model
def VGGCAM(nb_classes, num_input_channels=1024):
"""
Build Convolution Neural Network
args : nb_classes (int) number of classes
returns : model (keras NN) the Neural Net model
"""
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
# Add another conv layer with ReLU + GAP
model.add(Convolution2D(num_input_channels, 3, 3, activation='relu', border_mode="same"))
model.add(AveragePooling2D((14, 14)))
model.add(Flatten())
# Add the W layer
model.add(Dense(nb_classes, activation='softmax'))
model.name = "VGGCAM"
return model
def vgg_std16_model():
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
# Add another conv layer with ReLU + GAP
model.add(Convolution2D(1024, 3, 3, activation='relu', border_mode="same"))
model.add(AveragePooling2D((14, 14)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.name = "VGGCAM"
return model
def VGG(nb_classes, img_dim, pretr_weights_file=None, model_name=None):
"""
Build Convolution Neural Network
args : nb_classes (int) number of classes
img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
"""
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=img_dim, name="zeropadding2d_1"))
model.add(Convolution2D(64, 3, 3, activation='relu', name="convolution2d_1"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_2"))
model.add(Convolution2D(64, 3, 3, activation='relu', name="convolution2d_2"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_1"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_3"))
model.add(Convolution2D(128, 3, 3, activation='relu', name="convolution2d_3"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_4"))
model.add(Convolution2D(128, 3, 3, activation='relu', name="convolution2d_4"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_2"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_5"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_5"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_6"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_6"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_7"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_7"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_3"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_8"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_8"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_9"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_9"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_10"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_10"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_4"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_11"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_11"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_12"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_12"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_13"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_13"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_5"))
model.add(Flatten(name="flatten_1"))
model.add(Dense(4096, activation='relu', name="dense_1"))
model.add(Dropout(0.5, name="dropout_1"))
model.add(Dense(4096, activation='relu', name="dense_2"))
model.add(Dropout(0.5, name="dropout_2"))
model.add(Dense(1000, activation='softmax', name="dense_3"))
if model_name:
model.name = model_name
else:
model.name = "VGG"
if pretr_weights_file:
model.load_weights(pretr_weights_file)
model.layers.pop()
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = []
model.add(Dense(nb_classes, activation='softmax', name="dense_4"))
# Freeze layers until specified number
# for k in range(freeze_until):
# model.layers[k].trainable = True
return model
def VGGCAM(nb_classes, img_dim, pretr_weights_file=None, model_name=None):
"""
Build VGGCAM network
args : nb_classes (int) number of classes
img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net model
"""
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=img_dim, name="zeropadding2d_1"))
model.add(Convolution2D(64, 3, 3, activation='relu', name="convolution2d_1"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_2"))
model.add(Convolution2D(64, 3, 3, activation='relu', name="convolution2d_2"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_1"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_3"))
model.add(Convolution2D(128, 3, 3, activation='relu', name="convolution2d_3"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_4"))
model.add(Convolution2D(128, 3, 3, activation='relu', name="convolution2d_4"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_2"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_5"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_5"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_6"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_6"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_7"))
model.add(Convolution2D(256, 3, 3, activation='relu', name="convolution2d_7"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_3"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_8"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_8"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_9"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_9"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_10"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_10"))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name="maxpooling2d_4"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_11"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_11"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_12"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_12"))
model.add(ZeroPadding2D((1, 1), name="zeropadding2d_13"))
model.add(Convolution2D(512, 3, 3, activation='relu', name="convolution2d_13"))
# Add another conv layer with ReLU + GAP
model.add(Convolution2D(1024, 3, 3, activation='relu', border_mode="same", name="convolution2d_14"))
model.add(AveragePooling2D((14, 14), name="average_pooling2d_1"))
model.add(Flatten(name="flatten_1"))
# Add the W layer
model.add(Dense(10, activation='softmax', name="dense_1"))
if model_name:
model.name = model_name
else:
model.name = "VGGCAM"
if pretr_weights_file:
with h5py.File(pretr_weights_file) as hw:
for k in range(hw.attrs['nb_layers']):
g = hw['layer_{}'.format(k)]
weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
model.layers[k].set_weights(weights)
if model.layers[k].name == "convolution2d_13":
break
return model
