I'm beginner of AI and trying to implement the CRNN model in Keras.
model.add(TimeDistributed(base_model, input_shape=(3,32,32,3)))
I understand that the above code creates 3 timesteps and uses a 32x32 RGB image.
Then, if I have 90 train_image and set batch size to 30, how does it work?
grouped 30 pieces and entered into the timestep
or
enter into timestep in order
or am I misunderstanding about batch size?
If you have 90 images and you want a batch size of 30, then your input_shape should be (90,30,32,32,3). Source: the docs https://keras.io/api/layers/recurrent_layers/time_distributed/
Batch size is the amount of samples you want to use in an iteration of learning, before your CRNN updates its internal parameters.
As you can see in your screenshots you’ve trained your model for one epoch, in 3 timesteps (when training your learning model, an epoch is one iteration through the entire dataset. 30 times 3 makes 90, the whole dataset).
Related
I would like to fit a keras model but instead of running through all the batches in each epoch, I want to shuffle the whole training dataset (at the start of each epoch) then train only on the first 20 batches.
I have tried using steps_per_epoch but that restarts the next epoch where the previous one left off. What I want to do is, for every epoch:
shuffle the training dataset
split into batches of size 100
train on the first 20 batches
Can I achieve this using keras? I suspect I may need to use tensorflow directly to achieve this rather than keras, but I'm not sure how to go about doing that.
I am working on a problem with little data. I am augmenting the training set, i.e. I am rotating my images by up to 12 degrees in both directions:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.rotate.html
Since I only have my work-PC to work with (only i5 CPU) my batch_size is small. So small in fact that I only process one image, with its rotations, per batch (ofc. only using tiny learning_rate).
What I need to know is, if the dropout gets updated per picture or per batch. Because if it is per batch, I would need to change tactics.
Dropout is updated per batch_size you define in model.fit(), if unspecified the default is 32.
Dropout
The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time
Step
A training step is one gradient update. In one step batch_size examples are processed
I built an image classification model (CNN) using tensorflow-keras. I have some new images which I need to feed into the same model in order to increase the accuracy of the existing model.
I tried using the following code. But it decreases the accuracy.
re_calibrated_model = loaded_model.fit_generator(new_training_set,
steps_per_epoch=int(stp),
epochs=int(epc),
validation_data=new_test_set,
verbose=1,
validation_steps = 50)
Is there any method that I can use to re-calibrate my CNN model?
Your new training session does not start from previous training accuracy if you use completely different dataset to do second training.
You need to feed (old_images+new_images) for your intention.
What I normally do is to train the CNN model on the first batch of images and save that model. If I need to "retrain" the model with additional images, I load the previous saved model from disk and apply the inputs (test and train) and call the fit method. As mentioned before this only works if your outputs are exactly the same i.e. if you are using the same input and output classes.
In my experience training models using different image batches does not necessarily make your model more or less accurate, but rather increase the training time with each batch. Since I am using a CPU to train, my training time is about 30% faster if I train two batches of 1000 images each as oppose to training one batch of 2000 images for example.
Are these 2 the same batch-size, or they have different meaning?
BATCH_SIZE=10
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.batch(BATCH_SIZE)
2nd
history = model.fit(train_ds,
epochs=EPOCHS,
validation_data=create_dataset(X_valid, y_valid_bin),
max_queue_size=1,
workers=1,
batch_size=10,
use_multiprocessing=False)
I'm having problem of Ram out of run...
Traning images example 333000
Ram 30GB
12 GB GPU
What should be the batch size for this?
Full Code Here
Data-Set (Batch Size)
Batch size only mean that how much data will pass through a pipeline you defined. In the case of Dateset batch size represent how much data will be passed to the model in one iteration. For-example if you form a data generator and set batch size 8. Now on every iteration data generator give 8 data records.
Model.fit (Batch Size)
and in the model.fit when we set batch size it means that model will calculate loss after passing data-records equal to the batch-size. If you know about deep learning models they will calculate a particular loss on feed forward and than through back propagation they improves themselves. Now if you set batch-size 8 in model.fit now 8 data-records are pass to the model and loss is calculated of that 8 data-records and than model improve from that loss.
Example:
Now if you set dateset batch-size equal to 4 and set model.fit batch-size equal to 8. Now your dateset generator has to iterate 2 times to give 8 images to model and model.fit only perform 1 iteration to calculate loss.
Ram Issue
what is your image size ? Try to reduce batch_size because step per epochs are not related to ram but batch size is. Because if you are giving 10 batch size than 10 images have to load onto the ram for processing and your ram is not capable of loading 10 images at the same time. Try to give batch size 4 or 2. that may help you
Apologies if my questions are relatively simple, but I have been approaching the TensorFlow bit recently with the aim to learn new skills.
In the example, but there are several things I can't get:
in the explore data section, the size of the datasets return as 60/10k respectively for train and test.
where the size of the train/test size declared?
packages like SkLearn allows this to be specified in percentage when invoking the split methods.
in the training model part, when the 5 epochs are trained, the 1875 number appear below.
- what is that?
- I was expecting the training to run over the 60k items, but even by multiplying 1875 by 5 the number doesn't reach the 10k.
Dataset is loaded using tensorflow datasets API
The source itself has the split of 60K (Train) and 10K (Test)
https://www.tensorflow.org/datasets/catalog/fashion_mnist
An Epoch is a complete run with all the training samples. The training is done in batches. In the example you refer to, a batch size of 32 is used. So to complete one epoch, 1875 batches (60000 / 32) are run.
Hope this helps.