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import tensorflow as tf
from IPython.display import Markdown, display
import numpy as np

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

y_train = tf.one_hot(y_train, 10)

XinyueZ

import tensorflow as tf
from tensorflow.keras.layers import Flatten
from IPython.display import Markdown, display
import matplotlib.pyplot as plt
%matplotlib inline

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

XinyueZ

import matplotlib.pyplot as plt
import cv2
import numpy as np

!wget  https://dl.dropbox.com/s/i1e1brycec3hy3k/pilot-view.jpeg -O pilot-view.png

# For this evalution, we assume that we load image with 3 channels.


def toRGB(img):

XinyueZ

import matplotlib.pyplot as plt
import cv2
import numpy as np

!wget  https://dl.dropbox.com/s/i1e1brycec3hy3k/pilot-view.jpeg -O pilot-view.png

# For this evalution, we assume that we load image with 3 channels.


def toRGB(img):

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disp("Dropout, overcome the overfitting in NN....");

function [X] = dropout(X, keep_prob)
    % Dropout some units from X.
    % (1 - keep_prob) of units will be dropped out.

    sz = size(X);

    mask = rand(sz);
    mask = mask < 0.8; % Element of mask will be set to 1 or 0 with probability 𝑘𝑒𝑒𝑝_𝑝𝑟𝑜𝑏

XinyueZ

# An approach to do F_Score without np, however, using np is quite effective.
def F_Score(pred_list, true_list):
  def bitwise(a, b, core): 
    return [int(core(pred, true)) for pred, true in zip(pred_list, true_list)]

  true_positive = bitwise(pred_list, true_list, lambda pred, true: pred==1 and true==1) 
  true_positive = sum(true_positive)

  false_positive = bitwise(pred_list, true_list, lambda pred, true: pred==1 and true==0) 
  false_positive = sum(false_positive)

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    "terminal.integrated.env.windows": {
        "PATH": "/usr/local/lib/octave/6.4.0/bin"
    },
    "octave-formatter.pythonPath": "/usr/bin/python3",

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{
    "octave-formatter.pythonPath": "/usr/bin/python3"
}