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Revert "Added huggingface for keras siamese contrastive (#923)" #976

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Revert "Added huggingface for keras siamese contrastive (#923)" #976

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Binary file removed examples/vision/img/siamese_network/siamese_network_12_0.png
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18 changes: 6 additions & 12 deletions examples/vision/ipynb/siamese_network.ipynb
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Expand Up @@ -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/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet\n",
"[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) 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/abs/1803.01485)."
"by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf)."
]
},
{
Expand Down Expand Up @@ -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(tf.data.AUTOTUNE)\n",
"train_dataset = train_dataset.prefetch(8)\n",
"\n",
"val_dataset = val_dataset.batch(32, drop_remainder=False)\n",
"val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)\n",
"val_dataset = val_dataset.prefetch(8)\n",
""
]
},
Expand Down Expand Up @@ -590,20 +590,14 @@
"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).\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) |"
"and [Writing a training loop from scratch](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch?hl=en)."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "siamese_network",
"name": "siamesenetwork",
"private_outputs": false,
"provenance": [],
"toc_visible": true
Expand Down
64 changes: 23 additions & 41 deletions examples/vision/md/siamese_network.md
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Expand Up @@ -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/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet
[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) 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/abs/1803.01485).
by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf).

---
## Setup
Expand Down Expand Up @@ -83,10 +83,10 @@ positive_images_path = cache_dir / "right"

<div class="k-default-codeblock">
```
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]
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.

```
</div>
Expand Down Expand Up @@ -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(tf.data.AUTOTUNE)
train_dataset = train_dataset.prefetch(8)

val_dataset = val_dataset.batch(32, drop_remainder=False)
val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
val_dataset = val_dataset.prefetch(8)

```

Expand Down Expand Up @@ -205,9 +205,7 @@ visualize(*list(train_dataset.take(1).as_numpy_iterator())[0])
```



![png](/img/examples/vision/siamese_network/siamese_network_12_0.png)

![png](/img/examples/vision/siamese_network/siamesenetwork_12_0.png)


---
Expand Down Expand Up @@ -245,14 +243,6 @@ 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

Expand Down Expand Up @@ -391,27 +381,27 @@ siamese_model.fit(train_dataset, epochs=10, validation_data=val_dataset)
<div class="k-default-codeblock">
```
Epoch 1/10
151/151 [==============================] - 35s 185ms/step - loss: 0.5050 - val_loss: 0.3854
151/151 [==============================] - 277s 2s/step - loss: 0.5014 - val_loss: 0.3719
Epoch 2/10
151/151 [==============================] - 26s 170ms/step - loss: 0.3936 - val_loss: 0.3630
151/151 [==============================] - 276s 2s/step - loss: 0.3884 - val_loss: 0.3632
Epoch 3/10
151/151 [==============================] - 26s 170ms/step - loss: 0.3722 - val_loss: 0.3382
151/151 [==============================] - 287s 2s/step - loss: 0.3711 - val_loss: 0.3509
Epoch 4/10
151/151 [==============================] - 26s 173ms/step - loss: 0.3491 - val_loss: 0.3475
151/151 [==============================] - 295s 2s/step - loss: 0.3585 - val_loss: 0.3287
Epoch 5/10
151/151 [==============================] - 26s 170ms/step - loss: 0.3451 - val_loss: 0.3252
151/151 [==============================] - 299s 2s/step - loss: 0.3420 - val_loss: 0.3301
Epoch 6/10
151/151 [==============================] - 26s 171ms/step - loss: 0.3153 - val_loss: 0.3185
151/151 [==============================] - 297s 2s/step - loss: 0.3181 - val_loss: 0.3419
Epoch 7/10
151/151 [==============================] - 26s 171ms/step - loss: 0.3118 - val_loss: 0.3255
151/151 [==============================] - 290s 2s/step - loss: 0.3131 - val_loss: 0.3201
Epoch 8/10
151/151 [==============================] - 26s 171ms/step - loss: 0.3079 - val_loss: 0.3087
151/151 [==============================] - 295s 2s/step - loss: 0.3102 - val_loss: 0.3152
Epoch 9/10
151/151 [==============================] - 26s 171ms/step - loss: 0.2974 - val_loss: 0.3082
151/151 [==============================] - 286s 2s/step - loss: 0.2905 - val_loss: 0.2937
Epoch 10/10
151/151 [==============================] - 26s 172ms/step - loss: 0.2793 - val_loss: 0.3075
151/151 [==============================] - 270s 2s/step - loss: 0.2921 - val_loss: 0.2952

<keras.callbacks.History at 0x7f04dff32e80>
<tensorflow.python.keras.callbacks.History at 0x7fc69064bd10>

```
</div>
Expand Down Expand Up @@ -441,9 +431,7 @@ anchor_embedding, positive_embedding, negative_embedding = (
```



![png](/img/examples/vision/siamese_network/siamese_network_22_0.png)

![png](/img/examples/vision/siamese_network/siamesenetwork_22_0.png)


Finally, we can compute the cosine similarity between the anchor and positive
Expand All @@ -467,8 +455,8 @@ print("Negative similarity", negative_similarity.numpy())

<div class="k-default-codeblock">
```
Positive similarity: 0.991258
Negative similarity 0.9886112
Positive similarity: 0.9940324
Negative similarity 0.9918252

```
</div>
Expand Down Expand Up @@ -498,9 +486,3 @@ 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) |
9 changes: 2 additions & 7 deletions examples/vision/siamese_network.py
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Expand Up @@ -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/abs/1503.03832) by Schroff et al,. 2015. In this example, we define the triplet
[FaceNet paper](https://arxiv.org/pdf/1503.03832.pdf) 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/abs/1803.01485).
by [Rosenfeld et al., 2018](https://arxiv.org/pdf/1803.01485v3.pdf).
"""

"""
Expand Down Expand Up @@ -412,9 +412,4 @@ 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) |
"""