fix cyclegan save error (keras-team#925)

* fix cyclegan save error * formatted * comment * remove * ipynb
satpalsr · Jul 5, 2022 · 747d64b · 747d64b
1 parent f10a038
commit 747d64b
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Showing 2 changed files with 42 additions and 34 deletions.
diff --git a/examples/generative/cyclegan.py b/examples/generative/cyclegan.py
@@ -24,14 +24,13 @@
 ## Setup
 """
 
+
 import os
 import numpy as np
 import matplotlib.pyplot as plt
-
 import tensorflow as tf
 from tensorflow import keras
 from tensorflow.keras import layers
-
 import tensorflow_addons as tfa
 import tensorflow_datasets as tfds
 
@@ -409,6 +408,14 @@ def __init__(
         self.lambda_cycle = lambda_cycle
         self.lambda_identity = lambda_identity
 
+    def call(self, inputs):
+        return (
+            self.disc_X(inputs),
+            self.disc_Y(inputs),
+            self.gen_G(inputs),
+            self.gen_F(inputs),
+        )
+
     def compile(
         self,
         gen_G_optimizer,
@@ -571,6 +578,8 @@ def on_epoch_end(self, epoch, logs=None):
 adv_loss_fn = keras.losses.MeanSquaredError()
 
 # Define the loss function for the generators
+
+
 def generator_loss_fn(fake):
     fake_loss = adv_loss_fn(tf.ones_like(fake), fake)
     return fake_loss

diff --git a/examples/generative/ipynb/cyclegan.ipynb b/examples/generative/ipynb/cyclegan.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -62,8 +62,7 @@
     "import tensorflow_datasets as tfds\n",
     "\n",
     "tfds.disable_progress_bar()\n",
-    "autotune = tf.data.AUTOTUNE\n",
-    ""
+    "autotune = tf.data.AUTOTUNE\n"
    ]
   },
   {
@@ -81,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -127,8 +126,7 @@
     "    # Only resizing and normalization for the test images.\n",
     "    img = tf.image.resize(img, [input_img_size[0], input_img_size[1]])\n",
     "    img = normalize_img(img)\n",
-    "    return img\n",
-    ""
+    "    return img\n"
    ]
   },
   {
@@ -142,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -175,8 +173,7 @@
     "    .cache()\n",
     "    .shuffle(buffer_size)\n",
     "    .batch(batch_size)\n",
-    ")\n",
-    ""
+    ")\n"
    ]
   },
   {
@@ -190,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -203,8 +200,7 @@
     "    zebra = (((samples[1][0] * 127.5) + 127.5).numpy()).astype(np.uint8)\n",
     "    ax[i, 0].imshow(horse)\n",
     "    ax[i, 1].imshow(zebra)\n",
-    "plt.show()\n",
-    ""
+    "plt.show()\n"
    ]
   },
   {
@@ -218,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -337,8 +333,7 @@
     "    x = tfa.layers.InstanceNormalization(gamma_initializer=gamma_initializer)(x)\n",
     "    if activation:\n",
     "        x = activation(x)\n",
-    "    return x\n",
-    ""
+    "    return x\n"
    ]
   },
   {
@@ -375,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -418,8 +413,7 @@
     "    x = layers.Activation(\"tanh\")(x)\n",
     "\n",
     "    model = keras.models.Model(img_input, x, name=name)\n",
-    "    return model\n",
-    ""
+    "    return model\n"
    ]
   },
   {
@@ -436,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -490,8 +484,7 @@
     "\n",
     "# Get the discriminators\n",
     "disc_X = get_discriminator(name=\"discriminator_X\")\n",
-    "disc_Y = get_discriminator(name=\"discriminator_Y\")\n",
-    ""
+    "disc_Y = get_discriminator(name=\"discriminator_Y\")\n"
    ]
   },
   {
@@ -508,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -533,6 +526,14 @@
     "        self.lambda_cycle = lambda_cycle\n",
     "        self.lambda_identity = lambda_identity\n",
     "\n",
+    "    def call(self, inputs):\n",
+    "        return (\n",
+    "            self.disc_X(inputs),\n",
+    "            self.disc_Y(inputs),\n",
+    "            self.gen_G(inputs),\n",
+    "            self.gen_F(inputs),\n",
+    "        )\n",
+    "\n",
     "    def compile(\n",
     "        self,\n",
     "        gen_G_optimizer,\n",
@@ -650,8 +651,7 @@
     "            \"F_loss\": total_loss_F,\n",
     "            \"D_X_loss\": disc_X_loss,\n",
     "            \"D_Y_loss\": disc_Y_loss,\n",
-    "        }\n",
-    ""
+    "        }\n"
    ]
   },
   {
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -697,8 +697,7 @@
     "                \"generated_img_{i}_{epoch}.png\".format(i=i, epoch=epoch + 1)\n",
     "            )\n",
     "        plt.show()\n",
-    "        plt.close()\n",
-    ""
+    "        plt.close()\n"
    ]
   },
   {
@@ -712,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -773,13 +772,13 @@
    "source": [
     "Test the performance of the model.\n",
     "\n",
-    "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/CycleGAN)",
+    "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/CycleGAN)\n",
     "and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/CycleGAN)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -793,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -805,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },