def predict(X1):
X1.resize((samples, neurons), refcheck=False)
result = sess.run(l5, feed_dict={x: X1, y: test_y, keep_prob: 1})
return result[:test_y.shape[0]]
features = 179
rows = 4600
LR = 0.0001
epochs = 301
Xavier = 0.8
beta = 0.0001
X, Y = read_dataset(features, rows, Type.epilepsy)
train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.20, random_state=5)
neurons = train_x.shape[1]
samples = train_x.shape[0]
keep_prob = tf.placeholder("float")
x = tf.placeholder(tf.float32, shape=[None, neurons])
y = tf.placeholder(tf.float32, shape=[None, 1])
W0 = tf.Variable(tf.truncated_normal([neurons, samples], seed=1), name="W0", dtype=tf.float32) * Xavier
b0 = tf.Variable(tf.zeros([samples, 1]), name="bias0", dtype=tf.float32)
W1 = tf.Variable(tf.truncated_normal([samples, neurons], seed=0), name="W1", dtype=tf.float32) * Xavier
b1 = tf.Variable(tf.zeros([samples, 1]), name="bias1", dtype=tf.float32)
W2 = tf.Variable(tf.truncated_normal([neurons, samples], seed=0), name="W2", dtype=tf.float32) * Xavier
b2 = tf.Variable(tf.zeros([samples, 1]), name="bias2", dtype=tf.float32)
W3 = tf.Variable(tf.truncated_normal([samples, samples], seed=0), name="W3", dtype=tf.float32) * Xavier
b3 = tf.Variable(tf.zeros([samples, 1]), name="bias3", dtype=tf.float32)
from NN3 import NN3
from sklearn.model_selection import train_test_split
from nn_utils import read_dataset
X, Y = read_dataset(180, 500)
dataset = train_test_split(
X, Y, test_size=0.3, random_state=1)
epochs = 6000
nn3 = NN3(dataset, epochs, print_step=600)
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from nn_utils import read_dataset
def predict(X1):
X1.resize((samples, neurons), refcheck=False)
result = sess.run(l4, feed_dict={x: X1, y: test_y, keep_prob: 1})
return result[:test_y.shape[0]]
X, Y = read_dataset(180, 1000)
train_x, test_x, train_y, test_y = train_test_split(X,
Y,
test_size=0.20,
random_state=5)
LR = 0.0001
epochs = 4000
neurons = train_x.shape[1]
samples = train_x.shape[0]
keep_prob = tf.placeholder("float")
Xavier = 0.3
x = tf.placeholder(tf.float32, shape=[None, neurons])
y = tf.placeholder(tf.float32, shape=[None, 1])
W0 = tf.Variable(tf.truncated_normal([neurons, samples], seed=1),
name="W0",
dtype=tf.float32) * Xavier
b0 = tf.Variable(tf.zeros([samples, 1]), name="bias0", dtype=tf.float32)
import numpy as np
from nn_utils import read_dataset
size = 1000
def sigmoid(x, deriv=False):
if (deriv == True):
return x * (1 - x)
return 1 / (1 + np.exp(-x))
X, y = read_dataset(180, 1000)
np.random.seed(5)
# synapses
w0 = 2 * np.random.random((X.size / X.__len__(), X.__len__())) - 1
w1 = 2 * np.random.random((X.__len__(), X.__len__())) - 1
w2 = 2 * np.random.random((X.__len__(), 1)) - 1
# training step
for j in xrange(60000):
# Calculate forward through the network.
l0 = X
l1 = sigmoid(np.dot(l0, w0))
l2 = sigmoid(np.dot(l1, w1))
l3 = sigmoid(np.dot(l2, w2))
# Error back propagation of errors using the chain rule.
l3_error = y - l3
def predict(X1):
X1.resize((samples, neurons), refcheck=False)
result = sess.run(l5, feed_dict={x: X1, y: test_y, keep_prob: 1})
return result[:test_y.shape[0]]
features = 179
rows = 4600
LR = 0.0001
epochs = 6000
Xavier = 0.85
beta = 0.0001
X, Y = read_dataset(features, rows, Type.tumor)
train_x, test_x, train_y, test_y = train_test_split(X,
Y,
test_size=0.20,
random_state=5)
neurons = train_x.shape[1]
samples = train_x.shape[0]
s = 2400
keep_prob = tf.placeholder("float")
x = tf.placeholder(tf.float32, shape=[None, neurons])
y = tf.placeholder(tf.float32, shape=[None, 1])
W0 = tf.Variable(tf.truncated_normal([neurons, samples], seed=1),
name="W0",
dtype=tf.float32) * Xavier
b0 = tf.Variable(tf.zeros([samples, 1]), name="bias0", dtype=tf.float32)
