I followed the tutorial to create visually graphical representation of CNN model using this: https://keras.io/visualization/
My code at the moment is as follows:
from keras.utils import plot_model
from keras.applications.resnet50 import ResNet50
import numpy as np
model = ResNet50(weights='imagenet')
plot_model(model, to_file='model.png')
When I use the aforementioned code I am able to create a graphical representation (using Graphviz) of ResNet50 and save it in 'model.png'. But I want to create block diagram of the CNN model with the layers instead. An example of my desired output is as follows:
Any idea how I can achieve the aforementioned block diagram programatically instead of just generating the graph diagram of the CNN model?
Here is a comprehensive list of existing solutions (not only for Keras): How do you visualize neural network architectures?.
Pay attention on Netron.
Related
If I want to implement a classifier using the sklearn library. Is there a way to save the model or convert the file into a saved tensorflow file in order to convert it to tensorflow lite later?
If you replicate the architecture in TensorFlow, which will be pretty easy given that scikit-learn models are usually rather simple, you can explicitly assign the parameters from the learned scikit-learn models to TensorFlow layers.
Here is an example with logistic regression turned into a single dense layer:
import tensorflow as tf
import numpy as np
from sklearn.linear_model import LogisticRegression
# some random data to train and test on
x = np.random.normal(size=(60, 21))
y = np.random.uniform(size=(60,)) > 0.5
# fit the sklearn model on the data
sklearn_model = LogisticRegression().fit(x, y)
# create a TF model with the same architecture
tf_model = tf.keras.models.Sequential()
tf_model.add(tf.keras.Input(shape=(21,)))
tf_model.add(tf.keras.layers.Dense(1))
# assign the parameters from sklearn to the TF model
tf_model.layers[0].weights[0].assign(sklearn_model.coef_.transpose())
tf_model.layers[0].bias.assign(sklearn_model.intercept_)
# verify the models do the same prediction
assert np.all((tf_model(x) > 0)[:, 0].numpy() == sklearn_model.predict(x))
It is not always easy to replicate a scikit model in tensorflow. For instance scitik has a lot of on the fly imputation libraries which will be a bit tricky to implement in tensorflow
I am attempting to update the pre-trained BERT model using an in house corpus. I have looked at the Huggingface transformer docs and I am a little stuck as you will see below.My goal is to compute simple similarities between sentences using the cosine distance but I need to update the pre-trained model for my specific use case.
If you look at the code below, which is precisely from the Huggingface docs. I am attempting to "retrain" or update the model and I assumed that special_token_1 and special_token_2 represent "new sentences" from my "in house" data or corpus. Is this correct? In summary, I like the already pre-trained BERT model but I would like to update it or retrain it using another in house dataset. Any leads will be appreciated.
import tensorflow as tf
import tensorflow_datasets
from transformers import *
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
SPECIAL_TOKEN_1="dogs are very cute"
SPECIAL_TOKEN_2="dogs are cute but i like cats better and my
brother thinks they are more cute"
tokenizer.add_tokens([SPECIAL_TOKEN_1, SPECIAL_TOKEN_2])
model.resize_token_embeddings(len(tokenizer))
#Train our model
model.train()
model.eval()
BERT is pre-trained on 2 tasks: masked language modeling (MLM) and next sentence prediction (NSP). The most important of those two is MLM (it turns out that the next sentence prediction task is not really that helpful for the model's language understanding capabilities - RoBERTa for example is only pre-trained on MLM).
If you want to further train the model on your own dataset, you can do so by using BERTForMaskedLM in the Transformers repository. This is BERT with a language modeling head on top, which allows you to perform masked language modeling (i.e. predicting masked tokens) on your own dataset. Here's how to use it:
from transformers import BertTokenizer, BertForMaskedLM
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased', return_dict=True)
inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
You can update the weights of BertForMaskedLM using loss.backward(), which is the main way of training PyTorch models. If you don't want to do this yourself, the Transformers library also provides a Python script which allows you perform MLM really quickly on your own dataset. See here (section "RoBERTa/BERT/DistilBERT and masked language modeling"). You just need to provide a training and test file.
You don't need to add any special tokens. Examples of special tokens are [CLS] and [SEP], which are used for sequence classification and question answering tasks (among others). These are added by the tokenizer automatically. How do I know this? Because BertTokenizer inherits from PretrainedTokenizer, and if you take a look at the documentation of its __call__ method here, you can see that the add_special_tokens parameter defaults to True.
I'm not familiar with using python as a ML tool and wanted to train the MNIST data set. I have downloaded the MNIST library using
pip install python-mnist but do not know what my next step should be. What would an import statement look like? Should I also import TensorFlow and or Keras to train the data?
I know the MNIST dataset is available in TensorFlow and Keras, however, importing via pip a necessary solution for my use case. Both TensorFlow and Keras have tutorials using the MNIST data set, but I was wondering if it is possible to use the data set without using their pre-downloaded library.
The import statement should look like this
from mnist import MNIST
mndata = MNIST('./dir_with_mnist_data_files')
images, labels = mndata.load_training()
then you can work directly with the arrays of raw images and labels.
I'm using the Keras VGG16 model.
I've seen it there is a preprocess_input method to use in conjunction with the VGG16 model. This method appears to call the preprocess_input method in imagenet_utils.py which (depending on the case) calls _preprocess_numpy_input method in imagenet_utils.py.
The preprocess_input has a mode argument which expects "caffe", "tf", or "torch". If I'm using the model in Keras with TensorFlow backend, should I absolutely use mode="tf"?
If yes, is this because the VGG16 model loaded by Keras was trained with images which underwent the same preprocessing (i.e. changed input image's range from [0,255] to input range [-1,1])?
Also, should the input images for testing mode also undergo this preprocessing? I'm confident the answer to the last question is yes, but I would like some reassurance.
I would expect Francois Chollet to have done it correctly, but looking at https://github.com/fchollet/deep-learning-models/blob/master/vgg16.py either he is or I am wrong about using mode="tf".
Updated info
#FalconUA directed me to the VGG at Oxford which has a Models section with links for the 16-layer model. The information about the preprocessing_input mode argument tf scaling to -1 to 1 and caffe subtracting some mean values is found by following the link in the Models 16-layer model: information page. In the Description section it says:
"In the paper, the model is denoted as the configuration D trained with scale jittering. The input images should be zero-centered by mean pixel (rather than mean image) subtraction. Namely, the following BGR values should be subtracted: [103.939, 116.779, 123.68]."
The mode here is not about the backend, but rather about on what framework the model was trained on and ported from. In the keras link to VGG16, it is stated that:
These weights are ported from the ones released by VGG at Oxford
So the VGG16 and VGG19 models were trained in Caffe and ported to TensorFlow, hence mode == 'caffe' here (range from 0 to 255 and then extract the mean [103.939, 116.779, 123.68]).
Newer networks, like MobileNet and ShuffleNet were trained on TensorFlow, so mode is 'tf' for them and the inputs are zero-centered in the range from -1 to 1.
In my experience in training VGG16 in Keras, the inputs should be from 0 to 255, subtracting the mean [103.939, 116.779, 123.68]. I've tried transfer learning (freezing the bottom and stack a classifier on top) with inputs centering from -1 to 1, and the results are much worse than 0..255 - [103.939, 116.779, 123.68].
Trying to use VGG16 myself again lately, i had troubles getting descent results by just importing preprocess_input from vgg16 like this:
from keras.applications.vgg16 import VGG16, preprocess_input
Doing so, preprocess_input by default is set to 'caffe' mode but having a closer look at keras vgg16 code, i noticed that weights name
'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
is referring to tensorflow twice. I think that preprocess mode should be 'tf'.
processed_img = preprocess_input(img, mode='tf')
I have repurposed an Inception V3 network using the transfer learning method, following this article.
For that, I removed the final network layer, and fed hundreds of images of my face into the network.
A new model was then sucessfully generated: inceptionv3-ft.model
Now I would like to load this model and use its fixed weights to apply my face as a 'theme' on a input image, like google-dream.
For that I am using a keras program, which loads models like so:
from keras.applications import inception_v3
# Build the InceptionV3 network with our placeholder.
# The model will be loaded with pre-trained ImageNet weights.
model = inception_v3.InceptionV3(weights='imagenet',
include_top=False)
dream = model.input
Full code here: https://github.com/keras-team/keras/blob/master/examples/deep_dream.py
So, how do I load and pass not a pre-trained but rather my RE-trained model weights?
simply:
from keras.models import load_model
model = load_model('inceptionv3-ft.model')
dream = model.input