sign language.py

# Predictions 
predictions = model.predict_classes(testing_images)
for i in range(len(predictions)):
    if(predictions[i] >= 9):
        predictions[i] += 1
predictions[:5]   

#Output
#array([ 6,  8, 11, 14, 18])

# Precision, recall, f1-score for all the classes
from sklearn.metrics import classification_report, confusion_matrix

classes = ["Class " + str(i) for i in range(26) if i != 9]
print(classification_report(data_test['label'], predictions, target_names = classes))


# Confusion Matrix for the model predictions
cm = confusion_matrix(data_test['label'],predictions)

plt.figure(figsize=(12,7))
g = sns.heatmap(cm, cmap='Reds',annot=True,
           fmt='')