Further processing of peak intensity and position data were performed using the Python packages imageio, numpy38, pandas, statsmodels and matplotlib. For the analysis of per bivalent relative total HEI10 intensities in Fig.1b(left), wild type cells of the appropriate stage (‘early’, ‘mid’ ...
#Load Dataset: X_Data, Y_Data#X_Data = Pandas DataFrame#Y_Data = Numpy Array or ListX_data,Y_data=explainx.dataset_heloc() Split dataset into training & testing. X_train,X_test,Y_train,Y_test=train_test_split(X_data,Y_data,test_size=0.3,random_state=0) ...
In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output probabilities. from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input import keras.backend as K import numpy as np import json import ...
# ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.pyimportshapimportnumpyasnp# select a set of background examples to take an expectation overbackground=x_train[np.random.choice(x_train.shape[0],100,replace=False)]# explain predictions of the model...
NumPy introduced oldest-supported-numpy: https://github.com/scipy/oldest-supported-numpy This can be used by packages instead of specifying a NumPy range when building. The package doesn't exist in conda-forge and instead numpy should be used without any versions specified. Additional information ...
In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output probabilities. from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input import keras.backend as K import numpy as np import json import ...
In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output probabilities. from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input import keras.backend as K import numpy as np import json import ...
fit(X_train, y_train) # or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ... # EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. Understand the model from interpret import show ebm_global = ebm.explain_global() show(ebm_global)...
In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output probabilities. from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input import keras.backend as K import numpy as np import json import ...
Deep learning example with GradientExplainer (TensorFlow/Keras/PyTorch models) Expected gradients combines ideas fromIntegrated Gradients, SHAP, andSmoothGradinto a single expected value equation. This allows an entire dataset to be used as the background distribution (as opposed to a single reference ...