| def quick_sort(arr): | |
| # make 2 groups | |
| left = [] | |
| right = [] | |
| if len(arr) <= 1: | |
| return arr | |
| # pick random number as pivot | |
| ref = random.choice(arr) |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| import tensorflow as tf | |
| x = tf.constant(10) | |
| y = tf.constant(2) | |
| z = tf.subtract(tf.divide(x, y), tf.cast(tf.constant(1), tf.float64)) | |
| with tf.Session() as sess: | |
| output = sess.run(z) | |
| print(output) |
| import numpy as np | |
| class NeuralNetwork(object): | |
| def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate): | |
| # Set number of nodes in input, hidden and output layers. | |
| self.input_nodes = input_nodes | |
| self.hidden_nodes = hidden_nodes | |
| self.output_nodes = output_nodes |