[ad_1] the error has resolved just replace bgr_image to augmented_image predictions = sess.run(prob_tensor, {input_node: [bgr_image] }) to predictions = sess.run(prob_tensor, {input_node: [augmented_image] }) [ad_2] solved can anybody help i have got an error NameError: name ‘bgr_image’ is not defined [closed]
[ad_1] I will approach your question like this: I assume you are firmly interested in big data database use already and have a real need for one, so instead of repeating textbooks upon textbooks of information about them, I will highlight some that meet your 5 requirements – mainly Cassandra and Hadoop. 1) The first … Read more
[ad_1] Pretty general question, requiring more than a stack overflow response. The first thing I’d consider is setting up a predictive algorithm like the linear regression you spoke of. You can also add a constant to it, as in mx+b where the B is the known quantity of food for reservations. So you would run … Read more
[ad_1] The keyword is “multilabel classification“. In the output layer you have multiple neurons, each neuron representing one of your classes. Now you should use a binary classification for each neuron independently. So if you have 3 classes, the output of your network could be [0.1, 0.8, 0.99] which means the following: The first class … Read more
[ad_1] # Using standard scikit-learn label encoder. from sklearn.preprocessing import LabelEncoder le = LabelEncoder() # Encode all string columns. Assuming all categoricals are of type str. for c in df.select_dtypes([‘object’]): print “Encoding column ” + c df[c] = le.fit_transform(df[c]) 3 [ad_2] solved how to encode only categorical data in a dataframe
[ad_1] Accessing values in a dictionary requires brackets [], not parentheses () (see this answer. From the code you’ve given us, it looks like grads(“db” + str(l+1)) needs to be changed to grads[“db” + str(l+1)] to fix your error. A few notes on style: The character lowercase ‘l‘ shouldn’t be used as a variable name … Read more
[ad_1] The best book unequivocally that has implementation of Machine Learning algorithms in Python is the “Introduction to Machine Learning with Python: A Guide for Data Scientists” by Andreas C. Müller. Machine Learning algorithms in Python can be used from a package called scikit-learn. This package has everything you need for Machine Learning. All the … Read more
[ad_1] I would say it does not. The lack of agreement with the earlier values for Jan-Sep is a bad sign. The large negative value for Nov suggests that there’s something wrong. What do these numbers represent? Is a negative value physically possible? It’s a mistake to treat a function like a black box. I … Read more
[ad_1] You have to flatten your lists as shown here, and then use classification_report from scikit-learn: correct = [[‘*’,’*’],[‘*’,’PER’,’*’,’GPE’,’ORG’],[‘GPE’,’*’,’*’,’*’,’ORG’]] predicted = [[‘PER’,’*’],[‘*’,’ORG’,’*’,’GPE’,’ORG’],[‘PER’,’*’,’*’,’*’,’MISC’]] target_names = [‘PER’,’ORG’,’MISC’,’LOC’,’GPE’] # leave out ‘*’ correct_flat = [item for sublist in correct for item in sublist] predicted_flat = [item for sublist in predicted for item in sublist] from sklearn.metrics import classification_report … Read more
[ad_1] Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches: https://www.coursera.org/course/ml Also to get a broader picture I suggest coursera’s DataSciense course: https://www.coursera.org/course/datasci Finally a good book is Mahout in action – it is more about solving practical matters with mahout … Read more
[ad_1] Outputs of a neural network are not probabilities (generally), so that could be a reason that you’re not getting the “1 – P” result you’re looking for. Now, if it’s simple logistic regression, you’d get probabilities as output, but I’m assuming what you said is true and you’re using a super-simple neural network. Also, … Read more
[ad_1] Have a look at the sci-kit learn documentation for Multinomial NB. It clearly specifies the structure of the input data while trainig model.fit() must match the structure of the input data while testing or scoring model.predict(). This means that you cannot use the same model for different dataset. The only way this is possible … Read more
[ad_1] What you are trying to do is called time series prediction (given data at time t-n, t-(n+1) … t-1: predict the state at time t) and is generally a task for a recurrent neural network. Here is the great blog post by Andrej Karpathy about the topic that you should have a look at. … Read more
[ad_1] Remove id feature, also check and remove any features which you think add no value to prediction (any other features like id) or features with unique values. Also check if there is any class imbalance (how many samples of each class are present in data, is there proper balance among the classes?). Then try … Read more
[ad_1] This might depend on the default value of the parameter drop_intermediate (default to true) of roc_curve(), which is meant for dropping suboptimal thresholds, doc here. You might prevent such behaviour by passing drop_intermediate=False, instead. Here’s an example: import numpy as np try: from sklearn.datasets import fetch_openml mnist = fetch_openml(‘mnist_784’, version=1, cache=True) mnist[“target”] = mnist[“target”].astype(np.int8) … Read more