Added huggingface for keras siamese contrastive (keras-team#923)

* added huggingface in py

* generated_notebook_and_md

* removed  lines 86_93

Author: 0xrushi

examples/vision/img/siamese_network/siamese_network_12_0.png

@@ -32,13 +32,13 @@
     "Our goal is for the model to learn to estimate the similarity between images.\n",
     "\n",
     "For the network to learn, we use a triplet loss function. You can find an introduction to triplet loss in the\n",
-    "[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) by Schroff et al,. 2015. In this example, we define the triplet\n",
+    "[FaceNet paper](https://arxiv.org/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet\n",
     "loss function as follows:\n",
     "\n",
     "`L(A, P, N) = max(\u2016f(A) - f(P)\u2016\u00b2 - \u2016f(A) - f(N)\u2016\u00b2 + margin, 0)`\n",
     "\n",
     "This example uses the [Totally Looks Like dataset](https://sites.google.com/view/totally-looks-like-dataset)\n",
-    "by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf)."
+    "by [Rosenfeld et al., 2018](https://arxiv.org/abs/1803.01485)."
    ]
   },
   {
@@ -227,10 +227,10 @@
     "val_dataset = dataset.skip(round(image_count * 0.8))\n",
     "\n",
     "train_dataset = train_dataset.batch(32, drop_remainder=False)\n",
-    "train_dataset = train_dataset.prefetch(8)\n",
+    "train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)\n",
     "\n",
     "val_dataset = val_dataset.batch(32, drop_remainder=False)\n",
-    "val_dataset = val_dataset.prefetch(8)\n",
+    "val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)\n",
     ""
    ]
   },
@@ -590,14 +590,20 @@
     "which records every operation that you perform inside it. In this example, we use it to access the\n",
     "gradients passed to the optimizer to update the model weights at every step. For more details, check out the\n",
     "[Intro to Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)\n",
-    "and [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch?hl=en)."
+    "and [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch?hl=en).\n",
+    "\n",
+    "\n",
+    "**Example available on HuggingFace**\n",
+    "| Trained Model | Demo |\n",
+    "| :--: | :--: |\n",
+    "| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Siamese%20Network-black.svg)](https://huggingface.co/keras-io/siamese-contrastive) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Siamese%20Network-black.svg)](https://huggingface.co/spaces/keras-io/siamese-contrastive) |"
    ]
   }
  ],
  "metadata": {
   "colab": {
    "collapsed_sections": [],
-   "name": "siamesenetwork",
+   "name": "siamese_network",
    "private_outputs": false,
    "provenance": [],
    "toc_visible": true

examples/vision/img/siamese_network/siamese_network_22_0.png

@@ -23,13 +23,13 @@ two of them will be similar (_anchor_ and _positive_ samples), and the third wil
 Our goal is for the model to learn to estimate the similarity between images.
 
 For the network to learn, we use a triplet loss function. You can find an introduction to triplet loss in the
-[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) by Schroff et al,. 2015. In this example, we define the triplet
+[FaceNet paper](https://arxiv.org/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet
 loss function as follows:
 
 `L(A, P, N) = max(‖f(A) - f(P)‖² - ‖f(A) - f(N)‖² + margin, 0)`
 
 This example uses the [Totally Looks Like dataset](https://sites.google.com/view/totally-looks-like-dataset)
-by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf).
+by [Rosenfeld et al., 2018](https://arxiv.org/abs/1803.01485).
 
 ---
 ## Setup
@@ -83,10 +83,10 @@ positive_images_path = cache_dir / "right"
 
 <div class="k-default-codeblock">
 ```
-zsh:1: command not found: gdown
-zsh:1: command not found: gdown
-unzip:  cannot find or open left.zip, left.zip.zip or left.zip.ZIP.
-unzip:  cannot find or open right.zip, right.zip.zip or right.zip.ZIP.
+Downloading...
+From: https://drive.google.com/uc?id=1EzBZUb_mh_Dp_FKD0P4XiYYSd0QBH5zW
+To: /mnt/hdd2/keras-io/scripts/tmp_8979114/right.zip
+100%|████████████████████████████████████████| 104M/104M [00:01<00:00, 66.8MB/s]
 
 ```
 </div>
@@ -171,10 +171,10 @@ train_dataset = dataset.take(round(image_count * 0.8))
 val_dataset = dataset.skip(round(image_count * 0.8))
 
 train_dataset = train_dataset.batch(32, drop_remainder=False)
-train_dataset = train_dataset.prefetch(8)
+train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
 
 val_dataset = val_dataset.batch(32, drop_remainder=False)
-val_dataset = val_dataset.prefetch(8)
+val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
 
 ```
 
@@ -205,7 +205,9 @@ visualize(*list(train_dataset.take(1).as_numpy_iterator())[0])
 ```
 
 
-![png](/img/examples/vision/siamese_network/siamesenetwork_12_0.png)
+    
+![png](/img/examples/vision/siamese_network/siamese_network_12_0.png)
+    
 
 
 ---
@@ -243,6 +245,14 @@ for layer in base_cnn.layers:
     layer.trainable = trainable
 ```
 
+<div class="k-default-codeblock">
+```
+Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
+94773248/94765736 [==============================] - 2s 0us/step
+94781440/94765736 [==============================] - 2s 0us/step
+
+```
+</div>
 ---
 ## Setting up the Siamese Network model
 
@@ -381,27 +391,27 @@ siamese_model.fit(train_dataset, epochs=10, validation_data=val_dataset)
 <div class="k-default-codeblock">
 ```
 Epoch 1/10
-151/151 [==============================] - 277s 2s/step - loss: 0.5014 - val_loss: 0.3719
+151/151 [==============================] - 35s 185ms/step - loss: 0.5050 - val_loss: 0.3854
 Epoch 2/10
-151/151 [==============================] - 276s 2s/step - loss: 0.3884 - val_loss: 0.3632
+151/151 [==============================] - 26s 170ms/step - loss: 0.3936 - val_loss: 0.3630
 Epoch 3/10
-151/151 [==============================] - 287s 2s/step - loss: 0.3711 - val_loss: 0.3509
+151/151 [==============================] - 26s 170ms/step - loss: 0.3722 - val_loss: 0.3382
 Epoch 4/10
-151/151 [==============================] - 295s 2s/step - loss: 0.3585 - val_loss: 0.3287
+151/151 [==============================] - 26s 173ms/step - loss: 0.3491 - val_loss: 0.3475
 Epoch 5/10
-151/151 [==============================] - 299s 2s/step - loss: 0.3420 - val_loss: 0.3301
+151/151 [==============================] - 26s 170ms/step - loss: 0.3451 - val_loss: 0.3252
 Epoch 6/10
-151/151 [==============================] - 297s 2s/step - loss: 0.3181 - val_loss: 0.3419
+151/151 [==============================] - 26s 171ms/step - loss: 0.3153 - val_loss: 0.3185
 Epoch 7/10
-151/151 [==============================] - 290s 2s/step - loss: 0.3131 - val_loss: 0.3201
+151/151 [==============================] - 26s 171ms/step - loss: 0.3118 - val_loss: 0.3255
 Epoch 8/10
-151/151 [==============================] - 295s 2s/step - loss: 0.3102 - val_loss: 0.3152
+151/151 [==============================] - 26s 171ms/step - loss: 0.3079 - val_loss: 0.3087
 Epoch 9/10
-151/151 [==============================] - 286s 2s/step - loss: 0.2905 - val_loss: 0.2937
+151/151 [==============================] - 26s 171ms/step - loss: 0.2974 - val_loss: 0.3082
 Epoch 10/10
-151/151 [==============================] - 270s 2s/step - loss: 0.2921 - val_loss: 0.2952
+151/151 [==============================] - 26s 172ms/step - loss: 0.2793 - val_loss: 0.3075
 
-<tensorflow.python.keras.callbacks.History at 0x7fc69064bd10>
+<keras.callbacks.History at 0x7f04dff32e80>
 
 ```
 </div>
@@ -431,7 +441,9 @@ anchor_embedding, positive_embedding, negative_embedding = (
 ```
 
 
-![png](/img/examples/vision/siamese_network/siamesenetwork_22_0.png)
+    
+![png](/img/examples/vision/siamese_network/siamese_network_22_0.png)
+    
 
 
 Finally, we can compute the cosine similarity between the anchor and positive
@@ -455,8 +467,8 @@ print("Negative similarity", negative_similarity.numpy())
 
 <div class="k-default-codeblock">
 ```
-Positive similarity: 0.9940324
-Negative similarity 0.9918252
+Positive similarity: 0.991258
+Negative similarity 0.9886112
 
 ```
 </div>
@@ -486,3 +498,9 @@ which records every operation that you perform inside it. In this example, we us
 gradients passed to the optimizer to update the model weights at every step. For more details, check out the
 [Intro to Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
 and [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch?hl=en).
+
+
+**Example available on HuggingFace**
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Siamese%20Network-black.svg)](https://huggingface.co/keras-io/siamese-contrastive) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Siamese%20Network-black.svg)](https://huggingface.co/spaces/keras-io/siamese-contrastive) |

examples/vision/ipynb/siamese_network.ipynb

@@ -19,13 +19,13 @@
 Our goal is for the model to learn to estimate the similarity between images.
 
 For the network to learn, we use a triplet loss function. You can find an introduction to triplet loss in the
-[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) by Schroff et al,. 2015. In this example, we define the triplet
+[FaceNet paper](https://arxiv.org/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet
 loss function as follows:
 
 `L(A, P, N) = max(‖f(A) - f(P)‖² - ‖f(A) - f(N)‖² + margin, 0)`
 
 This example uses the [Totally Looks Like dataset](https://sites.google.com/view/totally-looks-like-dataset)
-by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf).
+by [Rosenfeld et al., 2018](https://arxiv.org/abs/1803.01485).
 """
 
 """
@@ -412,4 +412,9 @@ def metrics(self):
 [Intro to Keras for researchers](https://keras.io/getting_started/intro_to_keras_for_researchers/)
 and [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch?hl=en).
 
+
+**Example available on HuggingFace**
+| Trained Model | Demo |
+| :--: | :--: |
+| [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Model-Siamese%20Network-black.svg)](https://huggingface.co/keras-io/siamese-contrastive) | [![Generic badge](https://img.shields.io/badge/%F0%9F%A4%97%20Spaces-Siamese%20Network-black.svg)](https://huggingface.co/spaces/keras-io/siamese-contrastive) |
 """