def optimization(infer, regularizer, rate_batch, learning_rate=0.001, reg=0.1, device="/cpu:0"):
global_step = tf.train.get_global_step()
assert global_step is not None
with tf.device(device):
cost_l2 = tf.nn.l2_loss(tf.subtract(infer, rate_batch))
penalty = tf.constant(reg, dtype=tf.float32, shape=[], name="l2")
cost = tf.add(cost_l2, tf.multiply(regularizer, penalty))
train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost, global_step=global_step)
return cost, train_op
def inference_svd(user_batch, item_batch, user_num, item_num, dim=5, device="/cpu:0"):
with tf.device("/cpu:0"):
bias_global = tf.get_variable("bias_global", shape=[])
w_bias_user = tf.get_variable("embd_bias_user", shape=[user_num])
w_bias_item = tf.get_variable("embd_bias_item", shape=[item_num])
# embedding_lookup 就是在w_bias_user 查找user_batch中表示的信息
bias_user = tf.nn.embedding_lookup(w_bias_user, user_batch, name="bias_user")
bias_item = tf.nn.embedding_lookup(w_bias_item, item_batch, name="bias_item")
w_user = tf.get_variable("embd_user", shape=[user_num, dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
w_item = tf.get_variable("embd_item", shape=[item_num, dim],
initializer=tf.truncated_normal_initializer(stddev=0.02))
embd_user = tf.nn.embedding_lookup(w_user, user_batch, name="embedding_user")
embd_item = tf.nn.embedding_lookup(w_item, item_batch, name="embedding_item")
with tf.device(device):
infer = tf.reduce_sum(tf.multiply(embd_user, embd_item), 1)
infer = tf.add(infer, bias_global)
infer = tf.add(infer, bias_user)
infer = tf.add(infer, bias_item, name="svd_inference")
regularizer = tf.add(tf.nn.l2_loss(embd_user), tf.nn.l2_loss(embd_item), name="svd_regularizer")
return infer, regularizer
def multilayer_perceptron(x, weights, biases):
# Hidden layer with RELU activation
#x = tf.nn.dropout(x, 0.8)
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
#dlayer_1 = tf.nn.dropout(layer_1, 0.5)
#layer_2 = tf.add(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_1, weights['out']) + biases['out']
# return out_layer
return layer_1
from src import tensorflow as tf
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
add = tf.add(a, b)
mul = tf.multiply(a, b)
with tf.Session() as sess:
# Run every operation with variable input
print("Addition with variables: %i" % sess.run(add, feed_dict={
a: 2,
b: 3
}))
print("Multiplication with variables: %i" %
sess.run(mul, feed_dict={
a: 2,
b: 3
}))
# output:
# Addition with variables: 5
# Multiplication with variables: 6
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.], [2.]])
product = tf.matmul(matrix1, matrix2)
with tf.Session() as sess:
result = sess.run(product)
print(result)
#result:
# 12
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Create Model
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
activation = tf.add(tf.multiply(X, W), b)
# Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
from src import tensorflow as tf
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2) # Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)
sess = tf.Session()
print(sess.run([node1,node2])) #[3.0, 4.0]
node3 = tf.add(node1,node2)
print(node3) # Tensor("Add:0", shape=(), dtype=float32)
print(sess.run(node3)) # 7.0
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a+b
print(sess.run(adder_node,{a:3,b:4})) #7.0
print(sess.run(adder_node,{a:[1,2],b:[3,4]})) #[ 4. 6.]
add_and_triple = a*3
print(sess.run(add_and_triple,{a:5})) #15.0
print(sess.run(add_and_triple,{a:[1,2]})) #[ 3. 6.]
W = tf.Variable([.3],dtype=tf.float32)
b = tf.Variable([-.3],dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W*x + b
init = tf.global_variables_initializer()
sess.run(init) # 这个时候变量才会被初始化
print(sess.run(linear_model, {x:[1,2,3,4]}))
# Import MNIST data
from src.tensorflow import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))),
reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initializing the variables
init = tf.global_variables_initializer()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# loop over test data
for i in range(len(Xte)):