Fix shape issue (#1905)

* fix torch guide

* trim .md output

guides/custom_train_step_in_torch.py

@@ -2,7 +2,7 @@
 Title: Customizing what happens in `fit()` with PyTorch
 Author: [fchollet](https://twitter.com/fchollet)
 Date created: 2023/06/27
-Last modified: 2023/06/27
+Last modified: 2024/08/01
 Description: Overriding the training step of the Model class with PyTorch.
 Accelerator: GPU
 """
@@ -397,7 +397,7 @@ def compile(self, d_optimizer, g_optimizer, loss_fn):
 
     def train_step(self, real_images):
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        if isinstance(real_images, tuple):
+        if isinstance(real_images, tuple) or isinstance(real_images, list):
             real_images = real_images[0]
         # Sample random points in the latent space
         batch_size = real_images.shape[0]

guides/ipynb/custom_train_step_in_torch.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
     "**Date created:** 2023/06/27<br>\n",
-    "**Last modified:** 2023/06/27<br>\n",
+    "**Last modified:** 2024/08/01<br>\n",
     "**Description:** Overriding the training step of the Model class with PyTorch."
    ]
   },
@@ -50,17 +50,6 @@
     "Let's see how that works."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install keras --upgrade --quiet"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -282,7 +271,7 @@
     "outputs = keras.layers.Dense(1)(inputs)\n",
     "model = CustomModel(inputs, outputs)\n",
     "\n",
-    "# We don't passs a loss or metrics here.\n",
+    "# We don't pass a loss or metrics here.\n",
     "model.compile(optimizer=\"adam\")\n",
     "\n",
     "# Just use `fit` as usual -- you can use callbacks, etc.\n",
@@ -531,7 +520,7 @@
     "\n",
     "    def train_step(self, real_images):\n",
     "        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "        if isinstance(real_images, tuple):\n",
+    "        if isinstance(real_images, tuple) or isinstance(real_images, list):\n",
     "            real_images = real_images[0]\n",
     "        # Sample random points in the latent space\n",
     "        batch_size = real_images.shape[0]\n",

guides/md/custom_train_step_in_torch.md

@@ -2,7 +2,7 @@
 
 **Author:** [fchollet](https://twitter.com/fchollet)<br>
 **Date created:** 2023/06/27<br>
-**Last modified:** 2023/06/27<br>
+**Last modified:** 2024/08/01<br>
 **Description:** Overriding the training step of the Model class with PyTorch.
 
 
@@ -142,14 +142,14 @@ model.fit(x, y, epochs=3)
 
 <div class="k-default-codeblock">
 ```
-Epoch 1/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - mae: 0.6533 - loss: 0.6036
+Epoch 1/3    
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3410 - loss: 0.1772
 Epoch 2/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 522us/step - mae: 0.4013 - loss: 0.2522
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3336 - loss: 0.1695
 Epoch 3/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - mae: 0.3813 - loss: 0.2256
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - mae: 0.3170 - loss: 0.1511
 
-<keras.src.callbacks.history.History at 0x299b7baf0>
+<keras.src.callbacks.history.History at 0x7f48a3255710>
 
 ```
 </div>
@@ -238,17 +238,17 @@ model.fit(x, y, epochs=5)
 <div class="k-default-codeblock">
 ```
 Epoch 1/5
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 461us/step - loss: 0.2470 - mae: 0.3953
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6173 - mae: 0.6607
 Epoch 2/5
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 456us/step - loss: 0.2386 - mae: 0.3910
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2340 - mae: 0.3883
 Epoch 3/5
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 456us/step - loss: 0.2359 - mae: 0.3901
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1922 - mae: 0.3517
 Epoch 4/5
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 480us/step - loss: 0.2013 - mae: 0.3572
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.1802 - mae: 0.3411
 Epoch 5/5
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 463us/step - loss: 0.1903 - mae: 0.3480
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - loss: 0.1862 - mae: 0.3505
 
-<keras.src.callbacks.history.History at 0x299c5eec0>
+<keras.src.callbacks.history.History at 0x7f48975ccbd0>
 
 ```
 </div>
@@ -328,13 +328,13 @@ model.fit(x, y, sample_weight=sw, epochs=3)
 <div class="k-default-codeblock">
 ```
 Epoch 1/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 499us/step - mae: 1.4332 - loss: 1.0769
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3216 - loss: 0.0827
 Epoch 2/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 520us/step - mae: 0.9250 - loss: 0.5614
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3156 - loss: 0.0803
 Epoch 3/3
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 502us/step - mae: 0.6069 - loss: 0.2653
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - mae: 0.3085 - loss: 0.0760
 
-<keras.src.callbacks.history.History at 0x299c82bf0>
+<keras.src.callbacks.history.History at 0x7f48975d7bd0>
 
 ```
 </div>
@@ -378,11 +378,23 @@ y = np.random.random((1000, 1))
 model.evaluate(x, y)
 ```
 
+
+  1/32 ━━━━━━━━━━━━━━━━━━━━  0s 2ms/step - mae: 0.8706 - loss: 0.9344
+
 <div class="k-default-codeblock">
 ```
- 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 325us/step - mae: 0.4427 - loss: 0.2993
+
+```
+</div>
+ 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - mae: 0.8959 - loss: 0.9952
+
+
 
-[0.2726495862007141, 0.42286917567253113]
+
+
+<div class="k-default-codeblock">
+```
+[1.0077838897705078, 0.8984771370887756]
 
 ```
 </div>
@@ -463,7 +475,7 @@ class GAN(keras.Model):
 
     def train_step(self, real_images):
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        if isinstance(real_images, tuple):
+        if isinstance(real_images, tuple) or isinstance(real_images, list):
             real_images = real_images[0]
         # Sample random points in the latent space
         batch_size = real_images.shape[0]
@@ -556,9 +568,8 @@ gan.fit(dataloader, epochs=1)
 
 <div class="k-default-codeblock">
 ```
- 1094/1094 ━━━━━━━━━━━━━━━━━━━━ 1582s 1s/step - d_loss: 0.3581 - g_loss: 2.0571
-
-<keras.src.callbacks.history.History at 0x299ce1840>
+ 1094/1094 ━━━━━━━━━━━━━━━━━━━━ 394s 360ms/step - d_loss: 0.2436 - g_loss: 4.7259
+<keras.src.callbacks.history.History at 0x7f489760a490>
 
 ```
 </div>