network runs

models/cnn_net.py

@@ -1,4 +1,3 @@
-
 '''
 
 
@@ -21,9 +20,42 @@ accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.flo
 '''
 
 import tensorflow as tf
+from tensorflow.keras import layers
 
 
+# some simple models
+def build_net_001(input_shape, n_classes):
+    assert len(input_shape) == 3, 'only support 3 channels'
+    model = tf.keras.Sequential()
+    model.add(tf.keras.layers.Conv2D(
+        input_shape=input_shape, filters=32, kernel_size=(3, 3), strides=(1, 1),
+        padding='valid', activation='relu'))
+    model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
+    model.add(tf.keras.layers.Flatten())
+    model.add(tf.keras.layers.Dense(32, activation='relu'))
+    model.add(tf.keras.layers.Dense(n_classes, activation='softmax'))
+    return model
+
+
+def build_net_002(input_shape, n_classes):
+    model = tf.keras.Sequential([
+        layers.Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), strides=(1, 1),
+                      padding='same', activation='relu'),
+        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
+        layers.Conv2D(filters=128, kernel_size=(3, 3), padding='same'),
+        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
+        layers.Conv2D(filters=256, kernel_size=(3, 3), padding='same'),
+        layers.MaxPool2D(pool_size=(2, 2), padding='same'),
+
+        layers.Flatten(),
+        layers.Dense(1024, activation='relu'),
+        layers.Dense(n_classes, activation='softmax')
+    ])
+    return model
+
+
+# some models wrapped into tf.keras.Model
 class CNNNet(tf.keras.Model):
 
-    def __init__(self.):
-        pass
+    def __init__(self):
+        pass