Working on a convolutional neural network with 6 classes and about 1500 image per class. The model that works best for me has given the results below, in previous models I have worked on has given much smoother results and the validation isn't as jittery, now it does start to smoothen towards the end with 0.99 for both validation and training. At some epochs the training gives and accuracy of 1.0 but again towards the end it remained at 0.99 …

I am trying to plot the graph of validation loss against no of epochs. I have managed to train my data with the no of epochs. However, I have difficulty deciding which variables to use to display the graph. Attached below are my codes: import torch import torch.nn as nn from torchvision.datasets import ImageFolder from torchvision import transforms import torchvision.models as models from torch.utils.data import Dataset, DataLoader import os import numpy as np from tqdm import tqdm from PIL import …

If anyone is there to answer these, that'll be great. I'm in the midst of a Final Year Project on LSTM. Currently, I’m stuck and confused over LSTM codes. There are 4 hyperparameters that I can play around with: Look back Batch size LSTM units No. of Epochs Can you explain what will happen to my results if I tune each of these hyperparameters? And also is it common if we get different results each time we run the codes?

will it need more epochs for training or it is not a necessary and what is the learning rate I should set for this data with optimizer adam?

I want to see how many steps does it take for my model to reach a certain accuracy.Say 90 percent on cifar10.How can I get this info from the keras model ? EDIT: accuracy in each epoch is accessible in history object fit() returns,but im looking for accuracy in each step Solution: I made a callback object that keeps loss in each step import pickle from tensorflow.keras.callbacks import Callback class LossHistory(Callback): def __init__(self,path='',name=''): super(Callback, self).__init__() self.path = path self.name=name self.accuracy …

I'm using tensorflow to train a network to do an image segmentation task, and I have a question about the behavior of model.fit between epochs, specifically: Is there any difference between calling model.fit with 512 epochs, and calling model.fit 512 times? Here's a simplified version of my code, in case it helps. First, some setup: # Create image generators for dataset augmentation imgGen = ImageDataGenerator(**data_augmentation_parameters) maskGen = ImageDataGenerator(**data_augmentation_parameters) seed = random.randint(0, 1000000000) imgIterator = imgGen.flow(img, seed=seed, shuffle=False, batch_size=batch_size) maskIterator = …

I have a training data that consists of 6011 images. Now , I have converted this data to tfrecord files , where each file consists of 128 records and there are 47 files ( note that the last file consists of 123 records only ) . So my question is how do I correctly set the batch size and steps per epoch for training the model ? Do I set the batch size to 128 ( since each file consists …

colleagues, actually I am kind'a new to NN, but hard trying.. I have data: Index: 40073 entries (excluded from training, UID) Columns: 484 entries dtypes: bool(468), float64(2), int64(13), object(1) I used only 478 arguments. The Y is moneySpend which can be >= 0 The code is below: newDropped = df.drop(["moneySpend","userAgent", "secondsToBuy", "hoursToBuy", "daysToBuy", "platform"], axis = 1) x_train, x_test, y_train, y_test = train_test_split(newDropped, df["moneySpend"], test_size=0.25, random_state=547) model = Sequential() dnn1.add(Dense(16, input_dim=478, activation='relu')) dnn1.add(Dense(8, activation='relu')) dnn1.add(Dense(1, activation='linear')) model.compile(loss='mse', optimizer='adam', metrics=['accuracy']) tb_callback …

So I have a scenario in which the training data is being generated in response to what the Neural Network backed actor is doing. In essence its giving feedback to the Neural Network based on all of its mistakes, as its performing them, and it doesn't matter how many mistakes it makes, it will generate more feedback. Would it not, given this is statistical grouping in essence, make more sense to back-propagate fewer times per piece of feedback ? Would …

I was able to convert the 9.2e18 AD to a date, but I am confused about the exact date. Which date is 9.2e18 AD and and 9.2e18 BC? Time span (absolute) - [9.2e18 BC, 9.2e18 AD] i.e +/- 9.2e18 years NumPy documentation, section "Datetime Units" under "Datetimes and Timedeltas" Code Meaning Time span (relative) Time span (absolute) Y year +/- 9.2e18 years [9.2e18 BC, 9.2e18 AD] M month +/- 7.6e17 years [7.6e17 BC, 7.6e17 AD] W week +/- 1.7e17 years …

I have the coco 2014 dataset and need to train it as training is around 82700 and testing is 40500. However, I got the same sentence with different values every time with model.pedict() as I used one epoch only. Now, how can I decide the right number of epochs? I am trying 20 epochs now, but without batch size. Is that right?

I have read on Internet that epochs is used to give the time for the model to converge but I don't know how ? . I was thinking that epochs is used because to train the model sufficient times . How does model convergence relates with epochs . Also tell me that why epochs is useful ?

One uses and train/test split to use their training data to get an idea of how many epochs to train with. If the validation accuracy starts going down while the training accuracy is still going up, this would show a sign of overfitting, so one should probably stop the number epochs around there. But should training with all the data take longer to overfit? If so, should one add a few epochs? And if so for that, is there a …

I am replicating, in Keras, the work of a paper where I know the values of epoch and batch_size. Since the dataset is quite large, I am using fit_generator. I would like to know what to set in steps_per_epoch given epoch value and batch_size. Is there a standard way?

I am trying to implement connectivity as a feature within my code, but am unsure of how to fix this error code. Here is my code up until the point of the error. import mne import matplotlib.pyplot as plt import numpy as np from mne.time_frequency import psd_welch from mne.connectivity import spectral_connectivity from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, plot_confusion_matrix from sklearn.metrics import confusion_matrix from sklearn.pipeline import make_pipeline from sklearn.preprocessing import FunctionTransformer from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import KFold, …

Is there a way to determine the number of forward and backward passes in the training of a neural network using python?

I'm a beginner in machine learning and want to train a CNN (for image recognition) with optimized hyperparameter like dropout rate, learning rate and number of epochs. The optimal hyperparameter I try to find via GridSearchCV from Scikit-learn. I have often read that GridSearchCV can be used in combination with early stopping, but I can not find a sample code in which this is demonstrated. With EarlyStopping I would try to find the optimal number of epochs, but I don't …

What is the logic of the epoch? For example, 1 time, 2 times, etc. I just do not know what else is working to give better results than I know.

I have a huge csv dataset with a size of 200 GB. I'm using CsvDataset to make dataset generator for loading data from the disk while training the model. I want all the data to be passed on each epoch. So, what should I pass in the parameters steps_per_epoch and validation steps. Here is my Keras model using the data_set. training_csvs = sorted(str(p) for p in pathlib.Path('.').glob("path-to-data/Train_DS/*/*.csv")) training_csvs training_dataset=tf.data.experimental.CsvDataset( training_csvs, record_defaults=defaults, compression_type=None, buffer_size=None, header=True, field_delim=',', # use_quote_delim=True, # na_value="", select_cols=selected_indices …

Hi everyone, the above graph is produced by a BiLSTM model i just trained and tested. I can't seem to interpret it while it is very different from the references that i acquired by googling online. The graph above has a plateau appearing at the very beginning of the value loss. Shall I set my epochs to smaller than 20? My model is like this: prepared_model = model.fit(X_train,y_train,batch_size=32,epochs=100,validation_data=(X_test,y_test), shuffle=False) and how do you interpret it? thank you guys.