Update lab-11-5-mnist_cnn_ensemble_layers.py

dropout rate = 1 - keep_prob
phutoan31299 · May 4, 2018 · 29646ec · 29646ec
1 parent 976102e
commit 29646ec
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/lab-11-5-mnist_cnn_ensemble_layers.py b/lab-11-5-mnist_cnn_ensemble_layers.py
@@ -44,23 +44,23 @@ def _build_net(self):
             pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],
                                             padding="SAME", strides=2)
             dropout1 = tf.layers.dropout(inputs=pool1,
-                                         rate=0.7, training=self.training)
+                                         rate=0.3, training=self.training)
 
             # Convolutional Layer #2 and Pooling Layer #2
             conv2 = tf.layers.conv2d(inputs=dropout1, filters=64, kernel_size=[3, 3],
                                      padding="SAME", activation=tf.nn.relu)
             pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],
                                             padding="SAME", strides=2)
             dropout2 = tf.layers.dropout(inputs=pool2,
-                                         rate=0.7, training=self.training)
+                                         rate=0.3, training=self.training)
 
             # Convolutional Layer #3 and Pooling Layer #3
             conv3 = tf.layers.conv2d(inputs=dropout2, filters=128, kernel_size=[3, 3],
                                      padding="SAME", activation=tf.nn.relu)
             pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2],
                                             padding="SAME", strides=2)
             dropout3 = tf.layers.dropout(inputs=pool3,
-                                         rate=0.7, training=self.training)
+                                         rate=0.3, training=self.training)
 
             # Dense Layer with Relu
             flat = tf.reshape(dropout3, [-1, 128 * 4 * 4])