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I have a dataset that has a unique identifier and other features. It looks like this ID LenA TypeA LenB TypeB Diff Score Response 123-456 51 M 101 L 50 0.2 0 234-567 46 S 49 S 3 0.9 1 345-678 87 M 70 M 17 0.7 0 I split it up into training and test data. I am trying to classify test data into two classes from a classifier trained on training data. I want the identifier in the training and testing dataset so I can map the predictions back to the IDs . Is there a way that I can assign the identifier column as a ID or non-predictor like we can do in Azure ML Studio or SAS? I am using the

I have been using the following functional API for an image classification task using CNN: def create_model(X_train, X_test): visible = Input(shape=(X_train.shape[0], X_train.shape[1], 1)) conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible) hidden1 = Dense(10, activation='relu')(pool2) output = Dense(1, activation='sigmoid')(hidden1) model = Model(inputs = visible, outputs = output) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) return model X_tr = np.reshape(X_train, (1,X_train.shape[0], X_train.shape[1], 1)) X_te = np.reshape(X_test, (1,X_test

I've seen other posts talking about this but anyone of these can help me. I am using jupyter notebook with Python 3.6.0 on windows x6 machine. I have a large dataset but I keep only a piece of it to run my models: This is a piece of code that i used: df = loan_2.reindex(columns= ['term_clean','grade_clean', 'annual_inc', 'loan_amnt', 'int_rate','purpose_clean','installment','loan_status_clean']) df.fillna(method= 'ffill').astype(int) from sklearn.preprocessing import Imputer from sklearn.preprocessing import StandardScaler imp = Imputer(missing_values='NaN', strategy='median', axis=0) array =

When executing the following lines, !pip install kaggle !kaggle competitions download -c dogs-vs-cats -p /content/ I got the following error messages, Traceback (most recent call last): File "/usr/local/bin/kaggle", line 7, in <module> from kaggle.cli import main File "/usr/local/lib/python3.6/dist-packages/kaggle/__init__.py", line 23, in <module> api.authenticate() File "/usr/local/lib/python3.6/dist-packages/kaggle/api/kaggle_api_extended.py", line 109, in authenticate self._load_config(config_data) File "/usr/local/lib/python3.6/dist-packages/kaggle/api/kaggle_api_extended.py", line 151,

I am working with python 3.6.5 and tensorflow 1.8.0 Nr of neurons are 10 at the moment, input in this example is 3 I have already build a recurrent neuronal network and now wanted to improve it. I need some help! Here is a little excerpt of the code to reproduce my error: You can also replace BasicRNN by LSTM or GRU to get the other messages. import numpy as np import tensorflow as tf batch_size = 10 nr_inputs = 3 nr_outputs = 4 nr_steps = 4 nr_layers = 2 def mini_batch ( Xdata, ydata, batch_size ) : global global_counter result = None Xbatch = np.zeros( shape=[batch_size, nr_steps, nr_inputs]

Earlier in the program, I calculate the matrices P and K. I need the eigenvalues of PKP . Since this takes a while (it's a 10000 x 10000 matrix), I want to save the result someplace so it's easily accessible even if I restart the computer. The last two lines of my program are: eigs=np.linalg.eig(P@K@P) np.savetxt('eigdata.txt',eigs) This triggers the error: ValueError: could not broadcast input array from shape (10000,10000) into shape (10000) Deleting the last time removes the error, so the problem is definitely with np.savetxt . How do I fix this? Thanks np.linalg.eig outputs two np.ndarray

I want to mount google drive on google Colab and I am using this command to mount the drive from google.colab import drive drive.mount('/content/drive/') but I am getting this error ValueError Traceback (most recent call last) <ipython-input-45-9667a744255b> in <module>() 1 from google.colab import drive ----> 2 drive.mount('content/drive/') /usr/local/lib/python3.6/dist-packages/google/colab/drive.py in mount(mountpoint, force_remount) 99 raise ValueError('Mountpoint must either be a directory or not exist') 100 if '/' in mountpoint and not _os.path.exists(_os.path.dirname(mountpoint)): -->