add book02.recognize_digits mlp train test

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

@@ -21,7 +21,7 @@ class TestCrossEntropyOp1(OpTest):
 
         self.inputs = {"X": X, "Label": label}
         self.outputs = {"Y": cross_entropy}
-        self.attrs = {"softLabel": False}
+        self.attrs = {"soft_label": False}
 
     def test_check_output(self):
         self.check_output()

python/paddle/v2/framework/tests/test_recognize_digits_mlp.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
+
+init_program = Program()
+program = Program()
+image = layers.data(
+    name='x',
+    shape=[784],
+    data_type='float32',
+    program=program,
+    init_program=init_program)
+
+hidden1 = layers.fc(input=image,
+                    size=128,
+                    act='relu',
+                    program=program,
+                    init_program=init_program)
+hidden2 = layers.fc(input=hidden1,
+                    size=64,
+                    act='relu',
+                    program=program,
+                    init_program=init_program)
+
+predict = layers.fc(input=hidden2,
+                    size=10,
+                    act='softmax',
+                    program=program,
+                    init_program=init_program)
+
+label = layers.data(
+    name='y',
+    shape=[1],
+    data_type='int32',
+    program=program,
+    init_program=init_program)
+
+cost = layers.cross_entropy(
+    input=predict, label=label, program=program, init_program=init_program)
+avg_cost = layers.mean(x=cost, program=program, init_program=init_program)
+
+sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
+opts = sgd_optimizer.minimize(avg_cost)
+
+BATCH_SIZE = 128
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=8192),
+    batch_size=BATCH_SIZE)
+
+place = core.CPUPlace()
+exe = Executor(place)
+
+exe.run(init_program, feed={}, fetch_list=[])
+
+PASS_NUM = 100
+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("int32")
+        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])
+        if out[0] < 5.0:
+            exit(0)  # if avg cost less than 5.0, we think our code is good.
+exit(1)