Tip 4 AI

• Remove spike
  • 
    
    
  • Options
    • Change network
      • I.e. use 2 layer LSTM instead of 1 layer LSTM
    • Increase the batch size
    • Remove outliers
• number of steps (tensorflow)
  
  • num_steps = (len(traindf) / args['batch_size']) / args['learning_rate']
• queue capacity (tensorflow)
  • Queue_capacity = batch_size * 10
    
• checkpoint (tensorflow)
  • save_checkpoints_steps = max(100, params["train_steps"] // 10)
    
• tf.data.Dataset (tensorflow)

  • num_parallel_X, prefetch

  • dataset.apply(

  • Tf.contrib.data.shuffle_and_repeat

  • Tf.contrib.data.map_and_batch

• Mirrored strategy (tensorflow)

  • distribution = tf.contrib.distribute.MirroredStrategy()

  • tf.estimator.RunConfig(train_distribute=distribution)
    

• Multi-label classification (tensorflow)
  • Use the loss functions listed below for reducing computation
    • tf.nn.sampled_softmax_loss, tf.nn.nce_loss
• Visualizing the convolutions and pooling (tensorflow)

  • Code

    import tensorflow as tf print(tf.__version__) mnist = tf.keras.datasets.fashion_mnist (training_images, training_labels), (test_images, test_labels) = mnist.load_data() training_images=training_images.reshape(60000, 28, 28, 1) training_images=training_images / 255.0 test_images = test_images.reshape(10000, 28, 28, 1) test_images=test_images/255.0 model = tf.keras.models.Sequential([   tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),   tf.keras.layers.MaxPooling2D(2, 2),   tf.keras.layers.Conv2D(64, (3,3), activation='relu'),   tf.keras.layers.MaxPooling2D(2,2),   tf.keras.layers.Flatten(),   tf.keras.layers.Dense(128, activation='relu'),   tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.summary() model.fit(training_images, training_labels, epochs=5) test_loss = model.evaluate(test_images, test_labels)    print(test_labels[:100])    import matplotlib.pyplot as plt f, axarr = plt.subplots(3,4) FIRST_IMAGE=1 SECOND_IMAGE=3 THIRD_IMAGE=4 CONVOLUTION_NUMBER = 1 from tensorflow.keras import models layer_outputs = [layer.output for layer in model.layers] activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs) for x in range(0,4):   f1 = activation_model.predict(test_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x]   axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')   axarr[0,x].grid(False)   f2 = activation_model.predict(test_images[SECOND_IMAGE].reshape(1, 28, 28, 1))[x]   axarr[1,x].imshow(f2[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')   axarr[1,x].grid(False)   f3 = activation_model.predict(test_images[THIRD_IMAGE].reshape(1, 28, 28, 1))[x]   axarr[2,x].imshow(f3[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')   axarr[2,x].grid(False)

• Early stopping (tensorflow)

  • Code

    import tensorflow as tf print(tf.__version__)   class myCallback(tf.keras.callbacks.Callback):   def on_epoch_end(self, epoch, logs={}):     if(logs.get('loss')<0.4):       self.model.stop_training = True     elif(logs.get('acc')>0.8):       self.model.stop_training = True   callbacks = myCallback() mnist = tf.keras.datasets.fashion_mnist (training_images, training_labels), (test_images, test_labels) = mnist.load_data() training_images=training_images/255.0 test_images=test_images/255.0 model = tf.keras.models.Sequential([   tf.keras.layers.Flatten(),   tf.keras.layers.Dense(512, activation=tf.nn.relu),   tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(training_images, training_labels, epochs=5, callbacks=[callbacks])