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)