save 2

test.py

@@ -1,67 +1,191 @@
 import tensorflow as tf
 import numpy as np
 
+from tensorflow import keras as k
 from tensorflow.keras import layers as kl
 from tensorflow.keras import models as km
 from tensorflow.keras import optimizers as ko
 
+CONV_SIZE = 128
+MPPT_SIZE = 1024
+DRPT_RATE = 0.3
+TOUT_AMNT = 2
+
+def conv(i, n, s, t = (1, 1)):
+    c = kl.Conv2D(n, s, padding = 'same', strides = t)(i)
+    b = kl.BatchNormalization()(c)
+    return kl.Activation('relu')(b)
+
+def avg(i, s):
+    return kl.AveragePooling2D(s, padding = 'same')(i)
+
+def max(i, s):
+    return kl.MaxPooling2D(s, padding = 'same')(i)
+
+def generate_start(i):
+    r1 = conv(i, CONV_SIZE, (3, 3))
+    r2 = conv(r1, CONV_SIZE, (3, 3))
+    r3 = conv(r2, CONV_SIZE, (3, 3))
+
+    m1 = kl.MaxPooling2D((3, 3))(r3)
+
+    r4 = conv(m1, CONV_SIZE, (1, 1))
+    r5 = conv(r4, CONV_SIZE, (3, 3))
+
+    return kl.MaxPooling2D((3, 3))(r5)
+
+def generate_type_a(i):
+    a11 = conv(i, CONV_SIZE, (1, 1), (3, 3))
+
+    a21 = avg(i, (3, 3))
+    a22 = conv(a21, CONV_SIZE, (1, 1))
+
+    a31 = conv(i, CONV_SIZE, (1, 1))
+    a32 = conv(a31, CONV_SIZE, (3, 3), (3, 3))
+
+    a41 = conv(i, CONV_SIZE, (1, 1))
+    a42 = conv(a41, CONV_SIZE, (3, 3))
+    a43 = conv(a42, CONV_SIZE, (3, 3), (3, 3))
+
+    return kl.Concatenate()([a11, a22, a32, a43])
+
+def generate_ab_bridge(i):
+    ab11 = conv(i, CONV_SIZE, (1, 1))
+    ab12 = conv(ab11, CONV_SIZE, (3, 3))
+    ab13 = conv(ab12, CONV_SIZE, (3, 3), (3, 3))
+
+    ab21 = conv(i, CONV_SIZE, (3, 3), (3, 3))
+
+    ab31 = max(i, (3, 3))
+
+    return kl.Concatenate()([ab13, ab21, ab31])
+
+def generate_type_b(i):
+    b11 = conv(i, CONV_SIZE, (1, 1))
+    b12 = conv(b11, CONV_SIZE, (7, 1))
+    b13 = conv(b12, CONV_SIZE, (1, 7))
+    b14 = conv(b13, CONV_SIZE, (7, 1))
+    b15 = conv(b14, CONV_SIZE, (1, 7), (3, 3))
+
+    b21 = conv(i, CONV_SIZE, (1, 1))
+    b22 = conv(b21, CONV_SIZE, (1, 7))
+    b23 = conv(b22, CONV_SIZE, (7, 1), (3, 3))
+
+    b31 = conv(i, CONV_SIZE, (1, 1), (3, 3))
+
+    b41 = avg(i, (3, 3))
+    b42 = conv(b41, CONV_SIZE, (1, 1))
+
+    return kl.Concatenate()([b15, b23, b31, b42])
+
+def generate_bc_bridge(i):
+    bc11 = conv(i, CONV_SIZE, (1, 1))
+    bc12 = conv(bc11, CONV_SIZE, (7, 1))
+    bc13 = conv(bc12, CONV_SIZE, (1, 7))
+    bc14 = conv(bc13, CONV_SIZE, (3, 3), (3, 3))
+
+    bc21 = conv(i, CONV_SIZE, (1, 1))
+    bc22 = conv(bc21, CONV_SIZE, (1, 1), (3, 3))
+
+    bc31 = max(i, (3, 3))
+
+    return kl.Concatenate()([bc14, bc22, bc31])
+
+def generate_aux(i):
+    u1 = kl.AveragePooling2D((5, 5))(i)
+    u2 = conv(u1, CONV_SIZE, (1, 1))
+    u3 = kl.Flatten()(u2)
+    u4 = kl.Dropout(DRPT_RATE)(u3)
+    u5 = kl.Dense(MPPT_SIZE)(u4)
+
+    return kl.Dense(TOUT_AMNT)(u5)
+
+def generate_type_c(i):
+    c11 = conv(i, CONV_SIZE, (1, 1))
+    c12 = conv(c11, CONV_SIZE, (3, 3))
+    c13 = conv(c12, CONV_SIZE, (1, 3), (3, 3))
+    c14 = conv(c12, CONV_SIZE, (3, 1), (3, 3))
+
+    c21 = conv(i, CONV_SIZE, (1, 1))
+    c22 = conv(c21, CONV_SIZE, (1, 3), (3, 3))
+    c23 = conv(c21, CONV_SIZE, (3, 1), (3, 3))
+
+    c31 = max(i, (3, 3))
+    c32 = conv(c31, CONV_SIZE, (1, 1))
+
+    c41 = conv(i, CONV_SIZE, (1, 1))
+
+    return kl.Concatenate()([c14, c23, c32, c41])
+
+def generate_finish(i):
+    f1 = kl.AveragePooling2D((5, 5))(i)
+    f2 = conv(f1, CONV_SIZE, (1, 1))
+    f3 = kl.Flatten()(f2)
+    f4 = kl.Dropout(DRPT_RATE)(f3)
+    f5 = kl.Dense(MPPT_SIZE)(f4)
+
+    return kl.Dense(TOUT_AMNT)(f5)
+
+'''
 def generate_inception_model(a = 5, b = 4, c = 2):
     # start
     i = kl.Input(shape = (300, 300, 3))
 
-    r1 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(i)
+    r1 = kl.Conv2D(CONV_SIZE, (3, 3), padding = 'same', activation = 'relu')(i)
-    r2 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r1)
+    r2 = kl.Conv2D(CONV_SIZE, (5, 5), padding = 'same', activation = 'relu')(r1)
-    r3 = kl.Conv2D(1024, (7, 7), padding = 'same', activation = 'relu')(r2)
+    r3 = kl.Conv2D(CONV_SIZE, (7, 7), padding = 'same', activation = 'relu')(r2)
 
     m1 = kl.MaxPooling2D((3, 3))(r3)
 
-    r4 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(m1)
+    r4 = kl.Conv2D(CONV_SIZE, (3, 3), padding = 'same', activation = 'relu')(m1)
-    r5 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r4)
+    r5 = kl.Conv2D(CONV_SIZE, (5, 5), padding = 'same', activation = 'relu')(r4)
 
     ia0 = kl.MaxPooling2D((2, 2))(r5)
 
     # a types
     for k in range(a):
-        exec(f"ia{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_1_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
 
-        exec(f"ia{k}_3_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_3_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
-        exec(f"ia{k}_3_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_3_1)")
+        exec(f"ia{k}_3_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_3_1)")
 
-        exec(f"ia{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
-        exec(f"ia{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_5_1)")
+        exec(f"ia{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_5_1)")
-        exec(f"ia{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_5_2)")
+        exec(f"ia{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ia{k}_5_2)")
 
-        exec(f"ia{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
+        exec(f"ia{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
-        exec(f"ia{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_7_1)")
+        exec(f"ia{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_7_1)")
-        exec(f"ia{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_7_2)")
+        exec(f"ia{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ia{k}_7_2)")
-        exec(f"ia{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ia{k}_7_3)")
+        exec(f"ia{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ia{k}_7_3)")
 
         exec(f"ia{k+1} = kl.Concatenate()([ia{k}_1_1, ia{k}_3_2, ia{k}_5_3, ia{k}_7_4])")
 
     # grid size reductor 1
-    iab_1 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
+    iab_1 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu', strides = (2,2))(eval(f"ia{a}"))
 
-    iab_2 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
+    iab_2 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
-    iab_3 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_2)
+    iab_3 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(iab_2)
-    iab_4 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_3)
+    iab_4 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu', strides = (2,2))(iab_3)
+
+    iab_5 = kl.MaxPooling2D((2, 2), padding = 'same')(eval(f"ia{a}"))
 
     iab_6 = kl.Concatenate()([iab_1, iab_4, iab_5])
 
     # b types
     for k in range(b):
         exec(f"ib{k}_1_1 = kl.MaxPooling2D((2, 2), padding = 'same')(iab_6)")
-        exec(f"ib{k}_1_2 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i)")
+        exec(f"ib{k}_1_2 = kl.Conv2D(CONV_SIZE, (1, 1), strides = (2,2), padding = 'same', activation = 'relu')(i)")
 
-        exec(f"ib{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_6)")
+        exec(f"ib{k}_3_1 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(iab_6)")
 
-        exec(f"ib{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
+        exec(f"ib{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
-        exec(f"ib{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
+        exec(f"ib{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
-        exec(f"ib{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
+        exec(f"ib{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
 
-        exec(f"ib{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
+        exec(f"ib{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
-        exec(f"ib{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
+        exec(f"ib{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
-        exec(f"ib{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
+        exec(f"ib{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
-        exec(f"ib{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
+        exec(f"ib{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
 
         exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_2, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
 
@@ -69,18 +193,18 @@ def generate_inception_model(a = 5, b = 4, c = 2):
 
     # c types
     for k in range(c):
-        exec(f"ic{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_1_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
 
-        exec(f"ic{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_3_1 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
 
-        exec(f"ic{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
-        exec(f"ic{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
+        exec(f"ic{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
-        exec(f"ic{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
+        exec(f"ic{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
 
-        exec(f"ic{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
+        exec(f"ic{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
-        exec(f"ic{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
+        exec(f"ic{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
-        exec(f"ic{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
+        exec(f"ic{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
-        exec(f"ic{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
+        exec(f"ic{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
 
         exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_1, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
 
@@ -88,6 +212,7 @@ def generate_inception_model(a = 5, b = 4, c = 2):
 
     o = eval(f"ic{c}")
     return tf.keras.Model(inputs = i, outputs = o)
+'''
 
 
 '''
@@ -111,6 +236,31 @@ inception_type_a = [a_i, a_o]
 tf.keras.Model(*inception_type_a)
 '''
 
-mod = generate_inception_model()
+gi = kl.Input((300, 300, 3))
+
+ds = generate_start(gi)
+
+for _ in range(5):
+    ds = generate_type_a(ds)
+
+ds = generate_ab_bridge(ds)
+
+for _ in range(4):
+    ds = generate_type_b(ds)
+
+uo = generate_aux(ds)
+
+go = generate_bc_bridge(ds)
+
+for _ in range(2):
+    go = generate_type_c(go)
+
+go = generate_finish(go)
+
+r = kl.Concatenate()([uo, go])
+
+
+mod = k.Model(inputs = gi, outputs = r)
+
 mod.summary()
 mod.compile(optimizer = ko.Lion(learning_rate = 0.001))