title: Tensorflow is not fully using my gpu ? It always used 2-3% of my gpu
body: <p>I installed <code>python 3.6</code> along with <code>cuda 10.0</code> and <code>cudnn</code> using:</p>
  
  <pre><code>tensorflow - pip install tensorflow-gpu
  </code></pre>
  
  <p>but it only takes 2-3% of my gpu (as shown in task manager).</p>
  
  <p>I am getting 5 mins per epoch in my 2060Super; when I run it on the terminal it selects the gpu but the time is taken per epoch is too much and, also, it is not utilizing the whole gpu.</p>
  
  <pre><code>#Building CNN
  from keras.models import Sequential
  from keras.layers import Conv2D
  from keras.layers import MaxPooling2D
  from keras.layers import Flatten
  from keras.layers import Dense
  #initialize the CNN
  classifier = Sequential()
  
  #step 1 - Convolution
  classifier.add(Conv2D(32,3,3,input_shape = (64,64,3), activation='relu'))
  #step 2 Pooling
  classifier.add(MaxPooling2D(pool_size=(2,2)))
  
  # Adding a second convolutional layer
  classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  
  #step3 Flattening
  classifier.add(Flatten())
  
  #Full Connection
  classifier.add(Dense(units = 128,activation='relu'))
  classifier.add(Dense(units = 1,activation='sigmoid'))
  
  #compile CNN
  classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
  
  #Fitting CNN to images
  from keras.preprocessing.image import ImageDataGenerator
  train_datagen = ImageDataGenerator(rescale = 1./255,
                                     shear_range = 0.2,
                                     zoom_range = 0.2,
                                     horizontal_flip = True)
  
  test_datagen = ImageDataGenerator(rescale = 1./255)
  
  training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                   target_size = (64, 64),
                                                   batch_size = 32,
                                                   class_mode = 'binary')
  
  test_set = test_datagen.flow_from_directory('dataset/test_set',
                                              target_size = (64, 64),
                                              batch_size = 32,
                                              class_mode = 'binary')
  
  classifier.fit_generator(training_set,
                           steps_per_epoch = 8000,
                           epochs = 25,
                           validation_data = test_set,
                           validation_steps = 2000)
  </code></pre>