add test_fit_a_line

python/paddle/v2/framework/tests/test_fit_a_line.py

+import paddle.v2 as paddle
+import paddle.v2.framework.layers as layers
+import paddle.v2.framework.core as core
+import paddle.v2.framework.optimizer as optimizer
+
+from paddle.v2.framework.framework import Program, g_program
+from paddle.v2.framework.executor import Executor
+
+import numpy as np
+
+program = Program()
+x = layers.data(name='x', shape=[13], data_type='float32', program=program)
+y_predict = layers.fc(input=x, size=1, act=None, program=program)
+
+y = layers.data(name='y', shape=[1], data_type='float32', program=program)
+
+cost = layers.square_error_cost(input=y_predict, label=y, program=program)
+avg_cost = layers.mean(x=cost, program=program)
+
+sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
+opts = sgd_optimizer.minimize(avg_cost)
+
+# print str(program)
+
+BATCH_SIZE = 2
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = core.CPUPlace()
+exe = Executor(place)
+
+PASS_NUM = 1
+for pass_id in range(PASS_NUM):
+    for data in train_reader():
+        x_data = np.array(map(lambda x: x[0], data)).astype("float32")
+        y_data = np.array(map(lambda x: x[1], data)).astype("float32")
+        y_data = np.expand_dims(y_data, axis=1)
+
+        tensor_x = core.LoDTensor()
+        tensor_x.set(x_data, place)
+
+        tensor_y = core.LoDTensor()
+        tensor_y.set(y_data, place)
+        outs = exe.run(program,
+                       feed={'x': tensor_x,
+                             'y': tensor_y},
+                       fetch_list=[avg_cost])
+        out = np.array(outs[0])
+        print out