1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
| import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import requests from tensorflow.python.framework import ops import os.path import csv
ops.reset_default_graph()
sess = tf.Session()
birth_weight_file = 'birth_weight.csv'
if not os.path.exists(birth_weight_file): birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/' + \ 'raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat' birth_file = requests.get(birthdata_url) birth_data = birth_file.text.split('\r\n') birth_header = birth_data[0].split('\t') birth_data = [[float(x) for x in y.split('\t') if len(x)>=1] for y in birth_data[1:] if len(y)>=1] with open(birth_weight_file, "w") as f: writer = csv.writer(f) writer.writerow(birth_header) writer.writerows(birth_data) f.close()
birth_data = [] with open(birth_weight_file, newline='') as csvfile: csv_reader = csv.reader(csvfile) birth_header = next(csv_reader) for row in csv_reader: birth_data.append(row)
birth_data = [[float(x) for x in row] for row in birth_data]
y_vals = np.array([x[0] for x in birth_data]) x_vals = np.array([x[1:8] for x in birth_data])
seed = 99 np.random.seed(seed) tf.set_random_seed(seed)
train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False) test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices))) x_vals_train = x_vals[train_indices] x_vals_test = x_vals[test_indices] y_vals_train = y_vals[train_indices] y_vals_test = y_vals[test_indices]
def normalize_cols(m): col_max = m.max(axis=0) col_min = m.min(axis=0) return (m-col_min) / (col_max - col_min) x_vals_train = np.nan_to_num(normalize_cols(x_vals_train)) x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))
batch_size = 25
x_data = tf.placeholder(shape=[None, 7], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
A = tf.Variable(tf.random_normal(shape=[7,1])) b = tf.Variable(tf.random_normal(shape=[1,1]))
model_output = tf.add(tf.matmul(x_data, A), b)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=model_output, labels=y_target))
my_opt = tf.train.GradientDescentOptimizer(0.01) train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer() sess.run(init)
prediction = tf.round(tf.sigmoid(model_output)) predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32) accuracy = tf.reduce_mean(predictions_correct)
loss_vec = [] train_acc = [] test_acc = [] for i in range(1500): rand_index = np.random.choice(len(x_vals_train), size=batch_size) rand_x = x_vals_train[rand_index] rand_y = np.transpose([y_vals_train[rand_index]]) sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}) loss_vec.append(temp_loss) temp_acc_train = sess.run(accuracy, feed_dict={x_data: x_vals_train, y_target: np.transpose([y_vals_train])}) train_acc.append(temp_acc_train) temp_acc_test = sess.run(accuracy, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])}) test_acc.append(temp_acc_test) if (i+1)%300==0: print('Loss = ' + str(temp_loss))
plt.plot(loss_vec, 'k-') plt.title('Cross Entropy Loss per Generation') plt.xlabel('Generation') plt.ylabel('Cross Entropy Loss') plt.show()
plt.plot(train_acc, 'k-', label='Train Set Accuracy') plt.plot(test_acc, 'r--', label='Test Set Accuracy') plt.title('Train and Test Accuracy') plt.xlabel('Generation') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.show()
|