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)):