reducing number of hidden neurons to 32

Author: neil-tan

examples/DigitRecognition/deep_mlp.cpp

@@ -1,13 +1,13 @@
 // Auto generated by utensor-cli
 
-#include "uTensor/ops/MatrixOps.hpp"
-#include "uTensor/core/context.hpp"
 #include "deep_mlp_weight.hpp"
-#include "uTensor/ops/MathOps.hpp"
 #include "uTensor/ops/ArrayOps.hpp"
+#include "uTensor/ops/MatrixOps.hpp"
+#include "deep_mlp.hpp"
 #include "uTensor/ops/NnOps.hpp"
+#include "uTensor/ops/MathOps.hpp"
 #include "uTensor/core/tensor.hpp"
-#include "deep_mlp.hpp"
+#include "uTensor/core/context.hpp"
 
 
 void get_deep_mlp_ctx(Context& ctx, Tensor* input_0) {
@@ -60,7 +60,7 @@ void get_deep_mlp_ctx(Context& ctx, Tensor* input_0) {
     ctx.eval();
 }
 {    
-    ctx.add(new BinaryTensor<uint8_t>({784,64}, inline_Variable_quantized_const_0), 
+    ctx.add(new BinaryTensor<uint8_t>({784,32}, inline_Variable_quantized_const_0), 
             "Variable_quantized_const:0", 
             1);
 }
@@ -101,7 +101,7 @@ void get_deep_mlp_ctx(Context& ctx, Tensor* input_0) {
     ctx.eval();
 }
 {    
-    ctx.add(new BinaryTensor<float>({64}, inline_Variable_1_0), 
+    ctx.add(new BinaryTensor<float>({32}, inline_Variable_1_0), 
             "Variable_1:0", 
             2);
 }
@@ -185,7 +185,7 @@ void get_deep_mlp_ctx(Context& ctx, Tensor* input_0) {
     ctx.eval();
 }
 {    
-    ctx.add(new BinaryTensor<float>({64,10}, inline_Variable_2_0), 
+    ctx.add(new BinaryTensor<float>({32,10}, inline_Variable_2_0), 
             "Variable_2:0", 
             2);
 }

examples/DigitRecognition/deep_mlp.py

@@ -30,13 +30,13 @@ def bias_variable(shape, name):
 
 # Fully connected 2 layer NN
 def deepnn(x):
-  W_fc1 = weight_variable([784, 64], name='W_fc1')
-  b_fc1 = bias_variable([64], name='b_fc1')
+  W_fc1 = weight_variable([784, 32], name='W_fc1')
+  b_fc1 = bias_variable([32], name='b_fc1')
   a_fc1 = tf.add(tf.matmul(x, W_fc1), b_fc1, name="zscore")
   h_fc1 = tf.nn.relu(a_fc1)
   layer1 = tf.nn.dropout(h_fc1, 0.50)
 
-  W_fc2 = weight_variable([64, 10], name='W_fc3')
+  W_fc2 = weight_variable([32, 10], name='W_fc3')
   b_fc2 = bias_variable([10], name='b_fc3')
   logits = tf.add(tf.matmul(layer1, W_fc2), b_fc2, name="logits")
   y_pred = tf.argmax(logits, 1, name='y_pred')
@@ -118,10 +118,10 @@ def main(_):
                       help='session check point (default: %(default)s)')
   parser.add_argument('-n', '--num-iteration', type=int,
                       dest='num_iter',
-                      default=20000,
+                      default=100000,
                       help='number of iterations (default: %(default)s)')
   parser.add_argument('--batch-size', dest='batch_size',
-                      default=50, type=int,
+                      default=150, type=int,
                       help='batch size (default: %(default)s)')
   parser.add_argument('--log-every-iters', type=int,
                       dest='log_iter', default=1000,