title: Not able to print correct confusion matrix and also in heatmap values are printing in example 2e+2, e+4 etc etc
body: <p>Not able to print confusion matrix correctly and also the in printing heatmap the values in some blocks or columns are printing in example 2e+2,e+4 etc etc. Please help me in this   </p>
  
  <p>import numpy as np 
      import matplotlib.pyplot as plt
      import seaborn as sns
      import pandas as pd
      from keras.models import Sequential
      from keras.layers import Convolution2D
      from keras.layers import MaxPooling2D
      from keras.layers import Flatten
      from keras.layers import Dense
      from sklearn.metrics import classification_report, confusion_matrix</p>
  
  <pre><code>from PIL import ImageFile
  ImageFile.LOAD_TRUNCATED_IMAGES = True
  
  # Initialising the CNN
  classifier = Sequential()
  
  # Step 1 - Convolution
  classifier.add(Convolution2D(64, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))
  
  # Step 2 - Pooling
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  # Adding a second convolutional layer
  classifier.add(Convolution2D(64, 3, 3, activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  
  classifier.add(Convolution2D(64, 3, 3, activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  
  # Step 3 - Flattening
  classifier.add(Flatten())
  
  # Step 4 - Full connection
  classifier.add(Dense(output_dim = 128, activation = 'relu'))
  classifier.add(Dense(output_dim = 10, activation = 'sigmoid'))
  
  # Compiling the CNN
  classifier.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  
  # Part 2 - Fitting the CNN to the images
  
  from keras.preprocessing.image import ImageDataGenerator
  
  train_datagen = ImageDataGenerator(rescale = 1./255,
                                     shear_range = 0.2,
                                     zoom_range = 0.4,
                                     horizontal_flip = True)
  
  test_datagen = ImageDataGenerator(rescale = 1./255)
  
  #importing training data
  
  training_set = train_datagen.flow_from_directory('Dataset/train',
                                                   target_size = (64,64),
                                                   batch_size = 64,
                                                   class_mode = 'categorical')
  
  #importing test data
  test_set = test_datagen.flow_from_directory('Dataset/test',target_size = (64,64),
                                              batch_size = 64,
                                              class_mode = 'categorical',shuffle=False)
  
  #storing all the history
  
  history = classifier.fit_generator(
          training_set,
          steps_per_epoch=20,
          epochs=5,
          validation_data=test_set,
          validation_steps=2000)
  print(history.history.keys())
  </code></pre>
  
  <p>#summarize accuracy </p>
  
  <pre><code>plt.plot(history.history['acc'])
  plt.plot(history.history['val_acc'])
  plt.title('model accuracy')
  plt.ylabel('accuracy')
  plt.xlabel('epoch')
  plt.legend(['train', 'test'], loc='upper left')
  plt.show()
  
  # summarize history for loss
  plt.plot(history.history['loss'])
  plt.plot(history.history['val_loss'])
  plt.title('model loss')
  plt.ylabel('loss')
  plt.xlabel('epoch')
  plt.legend(['train', 'test'], loc='upper left')
  plt.show()
  
  #Confution Matrix 
  Y_pred = classifier.predict_generator(test_set, steps=len(test_set), max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
  y_pred = np.argmax(Y_pred, axis=1)
  
  #assigning values 
  confusion=(confusion_matrix(test_set.classes, y_pred))
  confusion_df = pd.DataFrame(confusion,
                       index = ['Airplan','Car','Birds','Cats','Deer', 'Dogs','Frog', 'Horse','Ship','Truck'], 
                       columns = ['Airplan','Car','Birds','Cats','Deer', 'Dogs','Frog', 'Horse','Ship','Truck'])
  
  #heatmap    
  sns.heatmap(confusion_df, annot=True)
  print(confusion_df)
  
  #classification report
  print('Classification Report')
  target_names = ['Airplan','Car','Birds','Cats','Deer', 'Dogs','Frog', 'Horse','Ship','Truck']
  print(classification_report(test_set.classes, y_pred, target_names=target_names))
  </code></pre>