Steps for fine-tuning a network are as follow:
Add your custom network on top of an already trained base
network.
Freeze the base network.
Train the part you added.
Unfreeze some layers in the base network.
Jointly train both these layers and the part you added.
Now if the network architecture is simple as VGG16, we can simply unfreeze the base network from block5_conv1 (Conv2D) and re-train it.
VGG16 Architecture
But When the architecture is highly complex as InceptionResnetV2, where to start? Does anyone has any practical experience? Run the following code in python to see the model:
from keras.applications import InceptionResNetV2
conv_base = InceptionResNetV2(weights='imagenet',
include_top=False,
input_shape=(299, 299, 3))
conv_base.summary()
from keras.utils import plot_model
plot_model(conv_base, to_file='model.png')`
A very basic fine-tuning of model with InceptionResNetV2 will look like this:
from inception_resnet_v2 import InceptionResNetV2
# ImageNet classification
model = InceptionResNetV2()
model.predict(...)
# Finetuning on another 100-class dataset
base_model = InceptionResNetV2(include_top=False, pooling='avg')
# The first argument in the next line represents the number of classes
outputs = Dense(100, activation='softmax')(base_model.output)
model = Model(base_model.inputs, outputs)
model.compile(...)
model.fit(...)
This is a good place to start github.com/yuyang-huang/keras-inception-resnet-v2
Related
There are two method for using resnet of pytorch.
methods 1:
import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)
model.eval()
methods 2:
import torch
net = models.resnet50(pretrained=True)
Are they load the same model. If not what is the difference?
The only difference that there is between your models if you load them in that way it's the number of layers, since you're loading resnet18 with Torch Hub and resnet50 with Models (thus, also the pretrained weights). They behave differently, you can see more about that in this paper.
Torch Hub also lets you publish pretrained models in your repository, but since you're loading it from 'pytorch/vision:v0.10.0' (which is the same repository from which Models is loading the neural networks), there should be no difference between:
model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
and
model = models.resnet18(pretrained=True)
Let's suppose we want to use in our model the pre-trained weights of VGG16 up to the layer before the third max pooling and then add the layers of our choice, how could we make this happen?
VGG16 architecture overview
You can to create a new model with say, base_model (VGG model with loaded weights and the unwanted layers 'pop()'ped). Then add VGG and other layers of your choice to the empty sequential model
Is there any way that I can try to modify ResNet50 and VGG16 where my data(spectograms) is of the shape (64,256,2)?
I understand that I can take some layers out and modify them(output, dense) but I am not really sure for input channels.
Can anyone suggest a way to accommodate 2 channels in the models? Help is much appreciated!
You can use a different number of channels in the input (and a different height and width), but in this case, you cannot use the pretrained imagenet weights. You have to train from scratch. You can create them as follows:
from tensorflow import keras # or just import keras
vggnet = keras.applications.vgg16.VGG16(input_shape=(64,256,2), include_top=False, weights=None)
Note the weights=None argument. It means initialize weights randomly. If you have number of channels set to 3, you could use weights='imagenet', but in your case, you have 2 channels, so it won't work and you have to set it to None. The include_top=False is there for you to add final classification layers with different categories yourself. You could also create vgg19.VGG19 in the same way. For ResNet, you could similarly create it as follows:
resnet = keras.applications.resnet50.ResNet50(input_shape=(64, 256, 2), weights=None, include_top=False)
For other models and versions of vgg and resnet, please check here.
I'm using two differents neural networks sequential models in my python program.
One RNN model defined like this:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, CuDNNLSTM
ModelRNN = Sequential()
ModelRNN.add(CuDNNLSTM(150, return_sequences=True, batch_size=None, input_shape=(None,10)))
ModelRNN.add(CuDNNLSTM(150, return_sequences=True))
ModelRNN.add(Dense(100, activation='relu'))
ModelRNN.add(Dense(10, activation='relu'))
optimizer = tf.keras.optimizers.Adam(lr=0.001)
ModelRNN.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])
One Dense model defined like this :
ModelDense = Sequential()
ModelDense.add(Dense(380, batch_size=None, input_shape=(1,380), activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
ModelDense.add(Dense(380, activation='elu'))
optimizer = tf.keras.optimizers.Adam(lr=0.00025)
ModelDense.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['accuracy'])
So my problem is that the two networks will work together so i have to run both of them in the same tensorflow session BUT, i want to save them in two differents folders.
I don't really know if it's possible because i never really interested myself in how is working the tensorflow graphs at all and i only know that when i use my tensorflow saver, i only give as parameter my session and a path.
So my question is how can i separate the storage of my models in two folders ?
I want to do that because i want to be able to easily change my RNN without having to retrain both of my networks or without having to overwrite my trained RNN
If i'am not clear please ask me for more details
So my question is how can i separate the storage of my models in two
folders ?
The save method of Keras model accepts a file path, so different folders can be given for the individual models.
ModelRNN.save('folder1/<filename.h5>')
ModelDense.save('folder2/<filename.h5>')
https://www.tensorflow.org/tutorials/keras/save_and_restore_models#save_the_entire_model
I'm trying to build some app using Transfer Learning. I want to use Vgg16 so I've done sth like this:
vgg16_model = keras.applications.vgg16.VGG16() but I want to transfer layers from Vgg16 to my model.
model = Sequential(layers=vgg16_model.layers) (I've seen this here)
but it leads me to error
TypeError: The added layer must be an instance of class Layer. Found:
tensorflow.python.keras.engine.input_layer.InputLayer
How can I init my Sequential model by vgg16 layers?
Thanks in advance.
Try this:
vgg = VGG16()
model = Sequential()
model.add(vgg)
model.add(...) # add additional layers