nb_filter += growth_rate
return concat_feat, nb_filter
if __name__ == '__main__':
# Example to fine-tune on 3000 samples from Cifar10
img_rows, img_cols = 224, 224 # Resolution of inputs
channel = 3
num_classes = 10
batch_size = 8
nb_epoch = 10
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols)
# Load our model
model = densenet161_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# Start Fine-tuning
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)
return concat_feat, nb_filter
if __name__ == '__main__':
# Example to fine-tune on 3000 samples from Cifar10
img_rows, img_cols = 224, 224 # Resolution of inputs
channel = 3
num_classes = 10
batch_size = 16
nb_epoch = 10
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols)
# Load our model
model = densenet169_model(img_rows=img_rows,
img_cols=img_cols,
color_type=channel,
num_classes=num_classes)
# Start Fine-tuning
model.fit(
X_train,
Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
shuffle=True,
verbose=1,
