I'm look for tutorials on how to build a Tensorflow model that generates predictions from input, for example, generating sentences from a paragraph, then the loss is determined when compared to ground truth labels. Or generating a number of predictions for objects found in an image. The main idea is having undetermined number of predictions or labels.

I've trained a model and saved it in .h5 format. when I try loading it I received this error ValueError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_588/661726548.py in <module> 9 # returns a compiled model 10 # identical to the previous one ---> 11 reconstructed_model = keras.models.load_model("./custom_model.h5") ~\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs) 65 except Exception as e: # pylint: disable=broad-except 66 filtered_tb = _process_traceback_frames(e.__traceback__) ---> 67 raise e.with_traceback(filtered_tb) from None 68 finally: 69 del filtered_tb ~\Anaconda3\lib\site-packages\keras\utils\generic_utils.py in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name) 560 …

I am trying to create a regression model that predicts the box office success of a movie, with one of the explanatory variables being the actors who appear in the film. My problem is that I decided to do the first 4 billed actors, but in the model, it is taking it as 4 separate variables (Actor 1, Actor 2, Actor 3, Actor 4). For example, Jack Nicholson is the lead in "as good as it gets" so he would …

I’m trying to figure out how to massage data and model the following scenario: Customers at a restaurant rate the quality of the service between 1-10. I have data on individual interactions between the servers and customers. Say - length of interaction, type of interaction (refilling beverage, ordering, cleaning, etc). Hypothesis here is each interaction contributes to the final score. I want to build a model that tells me given an interaction, how does it move the score. My intuition …

I am trying to perform hyperparameter tuning on sklearn_crfsuite.CRF model. When I try to execute below code, it doesn't give any exception but it probably fails to perform fit. And due to which, if I try to get best estimator from grid search, it doesn't work. %%time # define fixed parameters and parameters to search crf = sklearn_crfsuite.CRF( algorithm='lbfgs', max_iterations=100, all_possible_transitions=True ) params_space = { "c1": [0,0.05,0.1, 0.25,0.5,1], "c2": [0,0.05,0.1, 0.25,0.5,1] } # use the same metric for evaluation f1_scorer …

I am using machine learning models to predict an ordinal variable (values: 1,2,3,4, and 5) using 7 different features. I posed this as a regression problem, so the final outputs of a model are continuous variables. So an evaluation box plot looks like this: I experiment with both linear (linear regression, linear SVMs) and nonlinear models (SVMs with RBF, Random forest, Gradient boosting machines ). The models are trained using cross-validation (~1600 samples), and 25% of the dataset is used …

I am working on a project to predict the proper staffing needed for a customer service team using historical data. I am new to machine learning, and I am not sure if my approach to this problem is the right one. First I saw it as a multiple linear regression, but the more I think of the outcome I want, the more I realize regression is not the solution. I have a sample historical data with these fields: Number of …

For a data science project, I first used a standardized scaler on data in python, ran random forest then plotted the tree. However, the values of the decisions are in their standardized form. How do I plot the unscaled data? Example: as is: decision node based on Age <= 2.04 desired: decision node based on Age <= 30

Is there a machine learning model (something like LSTM or 1D-CNN) that takes two time series of variable length as input and outputs a binary classification (True/False whether time series are of same label)? So the data would look something like the following date value label 2020-01-01 2 0 # first input time series 2020-01-02 1 0 # first input time series 2020-01-03 1 0 # first input time series 2020-01-01 3 1 # second input time series 2020-01-03 1 …

I'm working on forecasting daily volumes and have used time series model to check for data stationarity. However, I'm strugging at forecasting data with 90% accuracy. Right now variation is extremely high and I'm just unable to bring it down. I've used log method to transform my data. Please find the link to folder below which contains ipynb and csv files: https://drive.google.com/drive/folders/1QUJkTucLPIf2vjo2mRmoBU6be083dYpQ?usp=sharing Any help will be highly appreciable Thanks, Rahul

I am doing a lot of work with transfer learning at the moment (using keras and tensorflow if that is relevant). I am having a lot of issues in sufficiently summarizing the very large models. This post: How do you visualize neural network architectures? shows a lot of useful methods for visualizing architectures, and they are great for networks such VGG16, but none of them are reasonable to include in a report if the models are very large (such as …

hope you are enjoying good health,i am trying to built a simple neural network which has to predict a shear wave well log values from other well logs,but my model's is stuck in mean absolute error of 2.45 and it is not improving further,i have changed the number of neurons,learning rate,loss function but of no use. Here is my model: tf.random.set_seed(42) model=tf.keras.Sequential([ tf.keras.layers.Dense(22,activation='relu'), tf.keras.layers.Dense(1) ]) #commpiling: model.compile( loss=tf.losses.mae, optimizer=tf.optimizers.Adam(learning_rate=0.006), metrics=['mae'] ) #fitting: history=model.fit(x_train,y_train,epochs=1000,verbose=0,) #evaluation: model.evaluate(x_test,y_test) here is the boxplot of …

Fairly simple question, but something I've been unable to understand firmly by scouring the interwebs. After running a LR model using SKlearn, one of the key outputs is coef_ , along with intercept_. I understand that coef_ is a transformation matrix that fully describes the relationships of the model; and that performing the dot-product of the input data, with coef_ and adding intercept_ will produce the predicted values for your inputs. My question is: What is the equation that defines …

I am using a Keras LSTM model to try to pinpoint the highs and lows (relative high points and low points) in a chart (I need the actual coordinates to those highs and lows, not just an image). The training process has no errors in it but the prediction output is completely irrelevant to the training output. what I've done so far is, I created the output data by feeding the input data to an algorithm from Scipy, argrelextrema. For …

I would like to build a simple sentiment analysis classifier using logistic regression. I downloaded a list of positive and negative words from cs.uic.edu. There are more than 6000 words both positive and negative. Linear Classifier has the form: (Wikipedia Reference) $$\sum wj*xj$$ where $w$ is the weight of the vector $x$. So for example, if the weight of vector awesome is 3, then in the following sentence: Food is awesome and music is awesome. according to the formula, it …

SUMMARY I'm building a linear regression model using Scikit and noticing that the model "performance" (RMSE and max error, namely) varies depending on whether I use the default LR or whether I apply PolynomialFeature(degree=1). My understanding is that these outcomes should be identical, since they are both utilizing a single-order LR model, however, my error is consistently lower when using the PolyFeatures version. TLDR When I run the code below, the second chunk (polynomial = degree of 1) is consistently …

I am working on a data science project on an industrial machine. This machine has two heating infrastructures. (fuel and electricity). It uses these two heatings at the same time, and I am trying to estimate the temperature value that occurs in the thermocouple as a result of this heating. However, this heating process takes place with some delay/lag. In other words, the one-unit change I have made in fuel and electrical heating is reflected in the thermocouple hours later. …

I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early stopping, various learning rates, and also regularizers, but none of them have worked properly. any suggestions would be appreciated. here is my code and my outputs: optimizer = keras.optimizers.Adam(lr=1e-3) model = Sequential() model.add(LSTM(units=50, activation='relu', activity_regularizer=tf.keras.regularizers.l2(1e-2), return_sequences=True, input_shape=(x_train.shape[1], x_train.shape[2]))) model.add(Dropout(0.2)) model.add(LSTM(units=50, activation='relu', activity_regularizer=tf.keras.regularizers.l2(1e-2), return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(y_train.shape[1])) model.compile(optimizer=optimizer, loss='mae') callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3) history = …

I'm not sure if I have this backwards or not, so I'll explain a bit of what is going on. I want to use Unity's Barracuda api to use an onnx model for classification and detection (depending on the model). Some example projects I've found have a model and labels handy, so it's easy to map the outputs to the labels. ie, if the 5th element has the highest score, i can look up the 5th label and find it …

I want to understand how to analyse the loss and accuracy (any metric) graphs that we plot from the model training history. Here's my graph, What can we say from the slope of graph? Does it matter? As you can see the validation and training loss-accuracy are pretty much the same for the most part. What does this mean? Usually the val accuracy is higher than training accuracy in the beginning but we don't see that here. Am I doing …