神经网络入门——16实现一个反向传播

Posted 2020-11-01 一起来看流星雨

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反向传播练习

现在你来实现一个通过反向传播训练的神经网络，数据集就是之前的研究生院录取数据。通过前面所学你现在有能力完成这个练习：

你的目标是：

实现一个正向传播
实现反向传播算法

更新权重

import numpy as np
from data_prep import features, targets, features_test, targets_test

np.random.seed(21)

def sigmoid(x):
    """
    Calculate sigmoid
    """
    return 1 / (1 + np.exp(-x))


# Hyperparameters
n_hidden = 2  # number of hidden units
epochs = 900
learnrate = 0.005

n_records, n_features = features.shape
last_loss = None
# Initialize weights
weights_input_hidden = np.random.normal(scale=1 / n_features ** .5,
                                        size=(n_features, n_hidden))
weights_hidden_output = np.random.normal(scale=1 / n_features ** .5,
                                         size=n_hidden)

for e in range(epochs):
    del_w_input_hidden = np.zeros(weights_input_hidden.shape)
    del_w_hidden_output = np.zeros(weights_hidden_output.shape)
    for x, y in zip(features.values, targets):
        ## Forward pass ##
        # TODO: Calculate the output
        hidden_input = np.dot(x, weights_input_hidden)
        hidden_output = sigmoid(hidden_input)

        output = sigmoid(np.dot(hidden_output,
                                weights_hidden_output))

        ## Backward pass ##
        # TODO: Calculate the network\'s prediction error
        error = y - output

        # TODO: Calculate error term for the output unit
        output_error_term = error * output * (1 - output)

        ## propagate errors to hidden layer

        # TODO: Calculate the hidden layer\'s contribution to the error
        hidden_error = np.dot(output_error_term, weights_hidden_output)

        # TODO: Calculate the error term for the hidden layer
        hidden_error_term = hidden_error * hidden_output * (1 - hidden_output)

        # TODO: Update the change in weights
        del_w_hidden_output += output_error_term * hidden_output
        del_w_input_hidden += hidden_error_term * x[:, None]

    # TODO: Update weights
    weights_input_hidden += learnrate * del_w_input_hidden / n_records
    weights_hidden_output += learnrate * del_w_hidden_output / n_records

    # Printing out the mean square error on the training set
    if e % (epochs / 10) == 0:
        hidden_output = sigmoid(np.dot(x, weights_input_hidden))
        out = sigmoid(np.dot(hidden_output,
                             weights_hidden_output))
        loss = np.mean((out - targets) ** 2)

        if last_loss and last_loss < loss:
            print("Train loss: ", loss, "  WARNING - Loss Increasing")
        else:
            print("Train loss: ", loss)
        last_loss = loss

# Calculate accuracy on test data
hidden = sigmoid(np.dot(features_test, weights_input_hidden))
out = sigmoid(np.dot(hidden, weights_hidden_output))
predictions = out > 0.5
accuracy = np.mean(predictions == targets_test)
print("Prediction accuracy: {:.3f}".format(accuracy))

import numpy as np
import pandas as pd

admissions = pd.read_csv(\'binary.csv\')

# Make dummy variables for rank
data = pd.concat([admissions, pd.get_dummies(admissions[\'rank\'], prefix=\'rank\')], axis=1)
data = data.drop(\'rank\', axis=1)

# Standarize features
for field in [\'gre\', \'gpa\']:
    mean, std = data[field].mean(), data[field].std()
    data.loc[:,field] = (data[field]-mean)/std
    
# Split off random 10% of the data for testing
np.random.seed(21)
sample = np.random.choice(data.index, size=int(len(data)*0.9), replace=False)
data, test_data = data.ix[sample], data.drop(sample)

# Split into features and targets
features, targets = data.drop(\'admit\', axis=1), data[\'admit\']
features_test, targets_test = test_data.drop(\'admit\', axis=1), test_data[\'admit\']

admit,gre,gpa,rank
0,380,3.61,3
1,660,3.67,3
1,800,4,1
1,640,3.19,4
0,520,2.93,4
1,760,3,2
1,560,2.98,1
0,400,3.08,2
1,540,3.39,3
0,700,3.92,2
0,800,4,4
0,440,3.22,1
1,760,4,1
0,700,3.08,2
1,700,4,1
0,480,3.44,3
0,780,3.87,4
0,360,2.56,3
0,800,3.75,2
1,540,3.81,1
0,500,3.17,3
1,660,3.63,2
0,600,2.82,4
0,680,3.19,4
1,760,3.35,2
1,800,3.66,1
1,620,3.61,1
1,520,3.74,4
1,780,3.22,2
0,520,3.29,1
0,540,3.78,4
0,760,3.35,3
0,600,3.4,3
1,800,4,3
0,360,3.14,1
0,400,3.05,2
0,580,3.25,1
0,520,2.9,3
1,500,3.13,2
1,520,2.68,3
0,560,2.42,2
1,580,3.32,2
1,600,3.15,2
0,500,3.31,3
0,700,2.94,2
1,460,3.45,3
1,580,3.46,2
0,500,2.97,4
0,440,2.48,4
0,400,3.35,3
0,640,3.86,3
0,440,3.13,4
0,740,3.37,4
1,680,3.27,2
0,660,3.34,3
1,740,4,3
0,560,3.19,3
0,380,2.94,3
0,400,3.65,2
0,600,2.82,4
1,620,3.18,2
0,560,3.32,4
0,640,3.67,3
1,680,3.85,3
0,580,4,3
0,600,3.59,2
0,740,3.62,4
0,620,3.3,1
0,580,3.69,1
0,800,3.73,1
0,640,4,3
0,300,2.92,4
0,480,3.39,4
0,580,4,2
0,720,3.45,4
0,720,4,3
0,560,3.36,3
1,800,4,3
0,540,3.12,1
1,620,4,1
0,700,2.9,4
0,620,3.07,2
0,500,2.71,2
0,380,2.91,4
1,500,3.6,3
0,520,2.98,2
0,600,3.32,2
0,600,3.48,2
0,700,3.28,1
1,660,4,2
0,700,3.83,2
1,720,3.64,1
0,800,3.9,2
0,580,2.93,2
1,660,3.44,2
0,660,3.33,2
0,640,3.52,4
0,480,3.57,2
0,700,2.88,2
0,400,3.31,3
0,340,3.15,3
0,580,3.57,3
0,380,3.33,4
0,540,3.94,3
1,660,3.95,2
1,740,2.97,2
1,700,3.56,1
0,480,3.13,2
0,400,2.93,3
0,480,3.45,2
0,680,3.08,4
0,420,3.41,4
0,360,3,3
0,600,3.22,1
0,720,3.84,3
0,620,3.99,3
1,440,3.45,2
0,700,3.72,2
1,800,3.7,1
0,340,2.92,3
1,520,3.74,2
1,480,2.67,2
0,520,2.85,3
0,500,2.98,3
0,720,3.88,3
0,540,3.38,4
1,600,3.54,1
0,740,3.74,4
0,540,3.19,2
0,460,3.15,4
1,620,3.17,2
0,640,2.79,2
0,580,3.4,2
0,500,3.08,3
0,560,2.95,2
0,500,3.57,3
0,560,3.33,4
0,700,4,3
0,620,3.4,2
1,600,3.58,1
0,640,3.93,2
1,700,3.52,4
0,620,3.94,4
0,580,3.4,3
0,580,3.4,4
0,380,3.43,3
0,480,3.4,2
0,560,2.71,3
1,480,2.91,1
0,740,3.31,1
1,800,3.74,1
0,400,3.38,2
1,640,3.94,2
0,580,3.46,3
0,620,3.69,3
1,580,2.86,4
0,560,2.52,2
1,480,3.58,1
0,660,3.49,2
0,700,3.82,3
0,600,3.13,2
0,640,3.5,2
1,700,3.56,2
0,520,2.73,2
0,580,3.3,2
0,700,4,1
0,440,3.24,4
0,720,3.77,3
0,500,4,3
0,600,3.62,3
0,400,3.51,3
0,540,2.81,3
0,680,3.48,3
1,800,3.43,2
0,500,3.53,4
1,620,3.37,2
0,520,2.62,2
1,620,3.23,3
0,620,3.33,3
0,300,3.01,3
0,620,3.78,3
0,500,3.88,4
0,700,4,2
1,540,3.84,2
0,500,2.79,4
0,800,3.6,2
0,560,3.61,3
0,580,2.88,2
0,560,3.07,2
0,500,3.35,2
1,640,2.94,2
0,800,3.54,3
0,640,3.76,3
0,380,3.59,4
1,600,3.47,2
0,560,3.59,2
0,660,3.07,3
1,400,3.23,4
0,600,3.63,3
0,580,3.77,4
0,800,3.31,3
1,580,3.2,2
1,700,4,1
0,420,3.92,4
1,600,3.89,1
1,780,3.8,3
0,740,3.54,1
1,640,3.63,1
0,540,3.16,3
0,580,3.5,2
0,740,3.34,4
0,580,3.02,2
0,460,2.87,2
0,640,3.38,3
1,600,3.56,2
1,660,2.91,3
0,340,2.9,1
1,460,3.64,1
0,460,2.98,1
1,560,3.59,2
0,540,3.28,3
0,680,3.99,3
1,480,3.02,1
0,800,3.47,3
0,800,2.9,2
1,720,3.5,3
0,620,3.58,2
0,540,3.02,4
0,480,3.43,2
1,720,3.42,2
0,580,3.29,4
0,600,3.28,3
0,380,3.38,2
0,420,2.67,3
1,800,3.53,1
0,620,3.05,2
1,660,3.49,2
0,480,4,2
0,500,2.86,4
0,700,3.45,3
0,440,2.76,2
1,520,3.81,1
1,680,2.96,3
0,620,3.22,2
0,540,3.04,1
0,800,3.91,3
0,680,3.34,2
0,440,3.17,2
0,680,3.64,3
0,640,3.73,3
0,660,3.31,4
0,620,3.21,4
1,520,4,2
1,540,3.55,4
1,740,3.52,4
0,640,3.35,3
1,520,3.3,2
1,620,3.95,3
0,520,3.51,2
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