def linear_regression(): epoch_number = 10 learning_rate = 0.01 train_features = [1.0, 2.0, 3.0, 4.0, 5.0] train_labels = [10.0, 20.0, 30.0, 40.0, 50.0] weights = tf.Variable(0.0) bias = tf.Variable(0.0) x = tf.placeholder(tf.float32) y = tf.placeholder(tf.float32) predict = weights * x + bias loss = y - predict sgd_optimizer = tf.train.GradientDescentOptimizer(learning_rate) train_op = sgd_optimizer.minimize(loss) with tf.Session() as sess: for epoch_index in range(epoch_number): # Take one sample from train dataset sample_number = len(train_features) train_feature = train_features[epoch_index % sample_number] train_label = train_labels[epoch_index % sample_number] # Update model variables and print loss sess.run(train_op, feed_dict={x: train_feature, y: train_label}) loss_value = sess.run(loss, feed_dict={x: 1.0, y: 10.0}) print("Epoch: {}, loss: {}, weight: {}, bias: {}".format( epoch_index, loss_value, sess.run(weights), sess.run(bias)))
def main(): a = tf.placeholder(tf.float32) b = tf.constant(32.0) c = tf.add(a, b) sess = tf.Session() print(sess.run(c, feed_dict={a: 10})) print(sess.run(c, feed_dict={a.name: 10}))
def main(): sess = tf.Session() hello = tf.constant("Hello, MiniFlow!") print(sess.run(hello)) # "Hello, MiniFlow!" a = tf.constant(10) b = tf.constant(32) c = tf.add(a, b) print(sess.run(c)) # 42 sess = tf.Session() a = tf.placeholder(tf.float32) b = tf.constant(32.0) c = tf.add(a, b) print(sess.run(c, feed_dict={a: 10.0})) print(sess.run(c, feed_dict={a.name: 10.0}))
def main(): sess = tf.Session() # Add a = tf.constant(32.0) b = tf.constant(10.0) c = a + b print(sess.run(c)) # Should be 42.0 # Minus c = a - b print(sess.run(c)) # Should be 22.0 # Multiple c = a * b print(sess.run(c)) # Should be 320.0 # Divide c = a / b print(sess.run(c)) # Should 3.2
y2 = mf.relu(mf.matmul(y1,w2)+b2) #y2 = batch_average(y2) w3 = mf.Variable(mf.random_normal([hidden_layer_2, n_classes], mu=0.0, sigma=0.1), name = 'w3') b3 = mf.Variable(mf.random_normal([n_classes], mu=0.0, sigma=0.1), name = 'b3') y3 = mf.relu(mf.matmul(y2,w3)+b3) #y3 = batch_average(y3) loss = mf.reduce_sum(mf.square(y_-y3)) train_op = mf.GradientDescentOptimizer(learning_rate=0.005).minimize(loss) #train_op = mf.ExponentialDecay(learning_rate=0.01, decay_rate=0.01).minimize(loss) train_y = mf.onehot_encoding(train_y, 10)#normalization(train_y,10) test_y = mf.onehot_encoding(test_y, 10)#normalization(test_y,10) #feed_dict = {x:train_X, y_:normalization(train_y,10)} #eval = equal(argmax(y3,0),argmax(y_,0)) accurate = mf.equal(mf.argmax(y3,1), mf.argmax(y_,1)) with mf.Session() as sess: epoches = 3 batch_size = 300 batches = int(len(train_X)/batch_size) #remains = len(train_X) - batches*batch_size for step in range(epoches): for batch in range(batches): loss_value = 0 accuracy = 0 mse = 0 start = batch*batch_size end = (batch+1)*batch_size #for index in range(start,end): X = train_X[start:end] Y = train_y[start:end]