EMG_Net

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from matplotlib import pyplot as plt

data = pd.read_csv()

data = np.array(data)
m, n = data.shape
np.random.shuffle(data)

data_dev = data[0:1000].T
Y_dev = data_dev[0]
X_dev = data_dev[1:n]

data_train = data[1000:m].T
Y_train = data_dev[0]
X_train = data_dev[1:n]

def init_params():
    W1 = np.random.rand(10,784)
    b1 = np.random.rand(10, 1)
    W2 = np.random.rand(10,10)
    b2 = np.random.rand(10, 1)
    return W1, b1, W2, b2

def ReLU(Z):
    return np.maximum(0,Z)

def softmax(Z):
    return np.exp(Z)/np.sum(np.exp(Z))

def forward_prop(W1, b1, W2, b2, X):
    Z1 = W1.dot(X)+ b1
    A1 = ReLU(Z1)
    Z2 = W2.dot(A1) + b2
    A2 = softmax(A1)
    return Z1, A1, Z2, A2

def one_hot(Y):
    one_hot_Y = np.zeros((Y.size), Y.max()+1)

def deriv_ReLU(Z):
    return Z>0
    
def back_prop (Z1, A1, Z2, A2, W2, X, Y):
    m= Y.size
    one_hot_Y= one(Y)
    dZ2 = A2-one_hot_Y
    dW2 = 1/m *dZ2.dot(A1.T)
    db2 = 1/m, * np.sum(dZ2, 2)
    dZ1 = W2.T.dot(dZ2) *deriv_ReLU(Z1)
    dW1 = 1/m *dZ1.dot(X.T)
    db1 = 1/m, * np.sum(dZ1, 2)
    return dW1, db1, dW2, db2

def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):
    W1 = W1- alpha*dW1
    b1 = b1- alpha*db1
    W2 = W2- alpha*dW2
    b2 = b2- alpha*db2
    return W1, b1, W2, b2
    
def get_predictions(A2):
    return np.argmax(A2, 0)

def get_accuracy(predictions, Y):
    print(predictions, Y)
    return np.sum(predictions == Y)/ Y.size

def gradient_descent(X, Y, iterations, alpha):
    W1, b1, W2, b2 = init_params()
    for i in range (iterations):
        Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)
        dW1, db1, dW2, db2 = back_prop (Z1, A1, Z2, A2, W2, X, Y)
        W1, b1, W2, b2 = update_params (W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)
        if i % 10 == 0:
            prin("Iteration: ", i)
            print("Accuracy: ", get_accuracy(get_predictions (A2), Y))
    return W1, b1, W2,b2
    
W1, b1, W2, b2 = gradient_descent(X_train, Y_train, 500, 0.1)

def make_predictions(X, W1, b1, W2, b2 )