path_to_train = os.path.join(path_to_split_datasets, "train")
path_to_validation = os.path.join(path_to_split_datasets, "validation")
# get number of classes
sub_dirs = [
sub_dir for sub_dir in os.listdir(path_to_train)
if os.path.isdir(os.path.join(path_to_train, sub_dir))
]
num_classes = len(sub_dirs)
# parameters for CNN
if use_vgg:
base_model = VGG(include_top=False, weights=None, input_shape=(64, 64, 3))
else:
base_model = DenseNet(include_top=False,
weights=None,
input_shape=(64, 64, 3))
# add a global spatial average pooling layer
top_model = base_model.output
top_model = GlobalAveragePooling2D()(top_model)
# or just flatten the layers
# top_model = Flatten()(top_model)
# let's add a fully-connected layer
if use_vgg:
# only in VGG19 a fully connected nn is added for classfication
# DenseNet tends to overfitting if using additionally dense layers
top_model = Dense(2048, activation='relu')(top_model)
top_model = Dense(2048, activation='relu')(top_model)
# and a logistic layer
predictions = Dense(num_classes, activation='softmax')(top_model)
import numpy as np
import tensorflow.keras as keras
from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization
from tensorflow.keras.applications.densenet import DenseNet121 as DenseNet
from model_utils import SequentialConstructor, evaluate, pretty_print_history
model = SequentialConstructor([
DenseNet(weights='imagenet', include_top=False),
GlobalAveragePooling2D(),
BatchNormalization(),
Dropout(0.5),
Dense(512, activation='relu'),
Dropout(0.5),
Dense(256, activation='relu'),
Dropout(0.5)
],
output_shape=(2, ))
hist = evaluate(model,
model_name='densenet121-binary',
train_dir='data/proc/binary/train/224/')
pretty_print_history(hist)
# get number of classes
sub_dirs = [
sub_dir for sub_dir in os.listdir(path_to_train)
if os.path.isdir(os.path.join(path_to_train, sub_dir))
]
num_classes = len(sub_dirs)
# parameters for CNN
if use_vgg:
base_model = VGG(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
else:
base_model = DenseNet(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
# add a global spatial average pooling layer
top_model = base_model.output
top_model = GlobalAveragePooling2D()(top_model)
# or just flatten the layers
# top_model = Flatten()(top_model)
# let's add a fully-connected layer
if use_vgg:
# only in VGG19 a fully connected nn is added for classfication
# DenseNet tends to overfitting if using additionally dense layers
top_model = Dense(2048, activation='relu')(top_model)
top_model = Dense(2048, activation='relu')(top_model)
# and a logistic layer
predictions = Dense(num_classes, activation='softmax')(top_model)
input_tensor = Conv2D(3, (1, 1))(input_tensor)
if use_vgg:
base_model_imagenet = VGG(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
base_model = VGG(include_top=False,
weights=None,
input_tensor=input_tensor)
for i, layer in enumerate(base_model_imagenet.layers):
# we must skip input layer, which has no weights
if i == 0:
continue
base_model.layers[i+1].set_weights(layer.get_weights())
else:
base_model_imagenet = DenseNet(include_top=False,
weights='imagenet',
input_shape=(64, 64, 3))
base_model = DenseNet(include_top=False,
weights=None,
input_tensor=input_tensor)
for i, layer in enumerate(base_model_imagenet.layers):
# we must skip input layer, which has no weights
if i == 0:
continue
base_model.layers[i+1].set_weights(layer.get_weights())
# add a global spatial average pooling layer
top_model = base_model.output
top_model = GlobalAveragePooling2D()(top_model)
# or just flatten the layers
# top_model = Flatten()(top_model)