cnn based image classification added, minor changes to dygraph example (#877)

paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb

@@ -30,7 +30,7 @@
      "output_type": "stream",
      "text": [
       "0.0.0\n",
-      "edf5f3173a25ae2230e9619ab5426317b4bd7cde\n"
+      "7f2aa2db3c69cb9ebb8bae9e19280e75f964e1d0\n"
      ]
     }
    ],
@@ -62,16 +62,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-1.4396907  -0.59741247]\n",
-      " [ 1.4717171  -0.06998838]\n",
-      " [-1.2790705   0.4278928 ]\n",
-      " [ 1.1862146  -1.895377  ]]\n",
+      "[[ 0.40741017  0.2083312 ]\n",
+      " [-1.7567089   0.72117436]\n",
+      " [ 0.8870686  -1.1389219 ]\n",
+      " [ 1.1233491   0.34348443]]\n",
       "[1. 2.]\n",
-      "[[-0.4396907   1.4025875 ]\n",
-      " [ 2.4717171   1.9300116 ]\n",
-      " [-0.2790705   2.4278927 ]\n",
-      " [ 2.1862144   0.10462296]]\n",
-      "[-2.6345158  1.3317404 -0.4232849 -2.6045394]\n"
+      "[[ 1.4074101   2.208331  ]\n",
+      " [-0.75670886  2.7211742 ]\n",
+      " [ 1.8870686   0.86107814]\n",
+      " [ 2.1233492   2.3434844 ]]\n",
+      "[ 0.8240726  -0.31436014 -1.3907751   1.810318  ]\n"
      ]
     }
    ],
@@ -108,13 +108,13 @@
      "output_type": "stream",
      "text": [
       "0 +> [5 6 7]\n",
-      "1 -> [-3 -3 -3]\n",
-      "2 -> [-3 -1  3]\n",
-      "3 +> [ 5 13 33]\n",
-      "4 -> [-3 11 75]\n",
+      "1 +> [5 7 9]\n",
+      "2 +> [ 5  9 15]\n",
+      "3 -> [-3  3 21]\n",
+      "4 +> [ 5 21 87]\n",
       "5 +> [  5  37 249]\n",
       "6 -> [ -3  59 723]\n",
-      "7 -> [  -3  123 2181]\n",
+      "7 +> [   5  133 2193]\n",
       "8 -> [  -3  251 6555]\n",
       "9 -> [   -3   507 19677]\n"
      ]
@@ -179,21 +179,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 [87.28088]\n",
-      "200 [57.775795]\n",
-      "400 [42.70884]\n",
-      "600 [45.509155]\n",
-      "800 [29.966158]\n",
-      "1000 [11.885025]\n",
-      "1200 [16.888378]\n",
-      "1400 [3.5780585]\n",
-      "1600 [5.3149533]\n",
-      "1800 [4.501356]\n",
-      "2000 [3.022315]\n",
-      "2200 [1.7214009]\n",
-      "2400 [0.3694626]\n",
-      "2600 [0.31249344]\n",
-      "2800 [0.1450614]\n"
+      "0 [2.0915627]\n",
+      "200 [0.67530334]\n",
+      "400 [0.52042854]\n",
+      "600 [0.28010666]\n",
+      "800 [0.09739777]\n",
+      "1000 [0.09307177]\n",
+      "1200 [0.04252927]\n",
+      "1400 [0.03095707]\n",
+      "1600 [0.03022156]\n",
+      "1800 [0.01616007]\n",
+      "2000 [0.01069116]\n",
+      "2200 [0.0055158]\n",
+      "2400 [0.00195092]\n",
+      "2600 [0.00101116]\n",
+      "2800 [0.00192219]\n"
      ]
     }
    ],
@@ -205,9 +205,9 @@
     "\n",
     "model = MyModel(input_size, hidden_size)\n",
     "\n",
-    "loss_fn = paddle.nn.MSELoss(reduction='sum')\n",
-    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, \n",
-    "                                 parameter_list=model.parameters())\n",
+    "loss_fn = paddle.nn.MSELoss(reduction='mean')\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.01, \n",
+    "                                 parameters=model.parameters())\n",
     "\n",
     "for t in range(200 * (total_data // batch_size)):\n",
     "    idx = np.random.choice(total_data, batch_size, replace=False)\n",