snapshot...

modules/deeplearning/espcn.py

@@ -210,9 +210,9 @@ class ESPCN:
 
         self.n_chans = 1
 
-        self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
+        # self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
         # self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
-        # self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
+        self.X_img = tf.keras.Input(shape=(32, 32, self.n_chans))
 
         self.inputs.append(self.X_img)
 
@@ -414,20 +414,25 @@ class ESPCN:
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
-        print(conv.shape)
+        # conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        # print(conv.shape)
 
-        conv = tf.nn.depth_to_space(conv, block_size=2)
-        conv = tf.keras.layers.Activation(activation=activation)(conv)
+        conv = tf.keras.layers.Conv2DTranspose(4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
-        if NumClasses == 2:
-            activation = tf.nn.sigmoid  # For binary
-        else:
-            activation = tf.nn.softmax  # For multi-class
+        self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
 
-        # Called logits, but these are actually probabilities, see activation
-        self.logits = tf.keras.layers.Activation(activation=activation)(conv)
+        # conv = tf.nn.depth_to_space(conv, block_size=2)
+        # conv = tf.keras.layers.Activation(activation=activation)(conv)
+        # print(conv.shape)
+        #
+        # if NumClasses == 2:
+        #     activation = tf.nn.sigmoid  # For binary
+        # else:
+        #     activation = tf.nn.softmax  # For multi-class
+        #
+        # # Called logits, but these are actually probabilities, see activation
+        # self.logits = tf.keras.layers.Activation(activation=activation)(conv)
 
         print(self.logits.shape)