Allow for transfer learning maybe?

bob/learn/tensorflow/network/SimpleCNN.py

@@ -3,68 +3,71 @@ import tensorflow as tf
 
 def architecture(input_layer, mode=tf.estimator.ModeKeys.TRAIN,
                  kernerl_size=(3, 3), n_classes=2,
-                 data_format='channels_last'):
+                 data_format='channels_last', reuse=False):
 
     # Keep track of all the endpoints
     endpoints = {}
 
-    # Convolutional Layer #1
-    # Computes 32 features using a kernerl_size filter with ReLU activation.
-    # Padding is added to preserve width and height.
-    conv1 = tf.layers.conv2d(
-        inputs=input_layer,
-        filters=32,
-        kernel_size=kernerl_size,
-        padding="same",
-        activation=tf.nn.relu,
-        data_format=data_format)
-    endpoints['conv1'] = conv1
-
-    # Pooling Layer #1
-    # First max pooling layer with a 2x2 filter and stride of 2
-    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2,
-                                    data_format=data_format)
-    endpoints['pool1'] = pool1
-
-    # Convolutional Layer #2
-    # Computes 64 features using a kernerl_size filter.
-    # Padding is added to preserve width and height.
-    conv2 = tf.layers.conv2d(
-        inputs=pool1,
-        filters=64,
-        kernel_size=kernerl_size,
-        padding="same",
-        activation=tf.nn.relu,
-        data_format=data_format)
-    endpoints['conv2'] = conv2
-
-    # Pooling Layer #2
-    # Second max pooling layer with a 2x2 filter and stride of 2
-    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2,
-                                    data_format=data_format)
-    endpoints['pool2'] = pool2
-
-    # Flatten tensor into a batch of vectors
-    # TODO: use tf.layers.flatten in tensorflow 1.4 and above
-    pool2_flat = tf.contrib.layers.flatten(pool2)
-    endpoints['pool2_flat'] = pool2_flat
-
-    # Dense Layer
-    # Densely connected layer with 1024 neurons
-    dense = tf.layers.dense(
-        inputs=pool2_flat, units=1024, activation=tf.nn.relu)
-    endpoints['dense'] = dense
-
-    # Add dropout operation; 0.6 probability that element will be kept
-    dropout = tf.layers.dropout(
-        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
-    endpoints['dropout'] = dropout
-
-    # Logits layer
-    # Input Tensor Shape: [batch_size, 1024]
-    # Output Tensor Shape: [batch_size, 2]
-    logits = tf.layers.dense(inputs=dropout, units=n_classes)
-    endpoints['logits'] = logits
+    with tf.variable_scope('SimpleCNN', reuse=reuse):
+        # Convolutional Layer #1
+        # Computes 32 features using a kernerl_size filter with ReLU
+        # activation.
+        # Padding is added to preserve width and height.
+        conv1 = tf.layers.conv2d(
+            inputs=input_layer,
+            filters=32,
+            kernel_size=kernerl_size,
+            padding="same",
+            activation=tf.nn.relu,
+            data_format=data_format)
+        endpoints['conv1'] = conv1
+
+        # Pooling Layer #1
+        # First max pooling layer with a 2x2 filter and stride of 2
+        pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],
+                                        strides=2, data_format=data_format)
+        endpoints['pool1'] = pool1
+
+        # Convolutional Layer #2
+        # Computes 64 features using a kernerl_size filter.
+        # Padding is added to preserve width and height.
+        conv2 = tf.layers.conv2d(
+            inputs=pool1,
+            filters=64,
+            kernel_size=kernerl_size,
+            padding="same",
+            activation=tf.nn.relu,
+            data_format=data_format)
+        endpoints['conv2'] = conv2
+
+        # Pooling Layer #2
+        # Second max pooling layer with a 2x2 filter and stride of 2
+        pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],
+                                        strides=2, data_format=data_format)
+        endpoints['pool2'] = pool2
+
+        # Flatten tensor into a batch of vectors
+        # TODO: use tf.layers.flatten in tensorflow 1.4 and above
+        pool2_flat = tf.contrib.layers.flatten(pool2)
+        endpoints['pool2_flat'] = pool2_flat
+
+        # Dense Layer
+        # Densely connected layer with 1024 neurons
+        dense = tf.layers.dense(
+            inputs=pool2_flat, units=1024, activation=tf.nn.relu)
+        endpoints['dense'] = dense
+
+        # Add dropout operation; 0.6 probability that element will be kept
+        dropout = tf.layers.dropout(
+            inputs=dense, rate=0.4,
+            training=mode == tf.estimator.ModeKeys.TRAIN)
+        endpoints['dropout'] = dropout
+
+        # Logits layer
+        # Input Tensor Shape: [batch_size, 1024]
+        # Output Tensor Shape: [batch_size, 2]
+        logits = tf.layers.dense(inputs=dropout, units=n_classes)
+        endpoints['logits'] = logits
 
     return logits, endpoints
 
@@ -117,7 +120,6 @@ def model_fn(features, labels, mode, params=None, config=None):
     else:
         train_op = None
 
-
     return tf.estimator.EstimatorSpec(
         mode=mode,
         predictions=predictions,