""" import your model here """
import your_model as tf
""" your model should support the following code """
# create model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
# define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
W_grad = tf.gradients(cross_entropy, [W])[0]
train_step = tf.assign(W, W - 0.5 * W_grad)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# get the mnist dataset (use tensorflow here)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
""" import your model here """
import your_model as tf
import numpy as np
sess = tf.Session()
# adder
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b
ans = sess.run(adder_node, {a: 3, b: 4.5})
assert np.equal(ans, 7.5)
ans = sess.run(adder_node, {a: [1, 3], b: [2, 3]})
assert np.array_equal(ans, [3, 6])
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# input
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
# first layer
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# second layer
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
""" import your model here """
import your_model as tf
""" your model should support the following code """
import numpy as np
sess = tf.Session()
# linear model
W = tf.Variable([.5], dtype=tf.float32)
b = tf.Variable([1.5], dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b
# define error
y = tf.placeholder(tf.float32)
error = tf.reduce_sum(linear_model - y)
# run init
init = tf.global_variables_initializer()
sess.run(init)
# calc error
feed = {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}
# assign
fixW = tf.assign(W, [-1.0])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
ans = sess.run(error, feed)
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Parameters
learning_rate = 0.01
training_epochs = 10
batch_size = 100
display_step = 1
# Network Parameters
n_hidden_1 = 128 # 1st layer number of features
n_hidden_2 = 64 # 2nd layer number of features
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
# Create model
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
layer_1 = tf.matmul(x, weights['h1']) + biases['b1']
layer_1 = tf.nn.relu(layer_1)
# Hidden layer with RELU activation
layer_2 = tf.matmul(layer_1, weights['h2']) + biases['b2']
layer_2 = tf.nn.relu(layer_2)
# Output layer with linear activation
out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
return out_layer
