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}))
train_X = mf.DataUtils(filename=trainfile_X).getImage() train_y = mf.DataUtils(filename=trainfile_y).getLabel() test_X = mf.DataUtils(testfile_X).getImage() test_y = mf.DataUtils(testfile_y).getLabel() train_X = np.array(train_X) train_y = np.array(train_y) test_X = np.array(test_X) test_y = np.array(test_y) ###MLP with mf.Graph().as_default(): x = mf.placeholder() y_ = mf.placeholder() input_shape = train_X.shape[1] hidden_layer_1 = 256 hidden_layer_2 = 128 n_classes = 10 w1 = mf.Variable(mf.random_normal([input_shape,hidden_layer_1], mu=0.0, sigma=0.1), name = 'w1') b1 = mf.Variable(mf.random_normal([hidden_layer_1], mu=0.0, sigma=0.1), name = 'b1') y1 = mf.relu(mf.matmul(x,w1)+b1) #y1 = batch_average(y1) w2 = mf.Variable(mf.random_normal([hidden_layer_1, hidden_layer_2], mu=0.0, sigma=0.1), name = 'w2') b2 = mf.Variable(mf.random_normal([hidden_layer_2], mu=0.0, sigma=0.1), name = 'b2') y2 = mf.relu(mf.matmul(y1,w2)+b2) #y2 = batch_average(y2)