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Update the Segmentation Tutorial Colab to be compatible with Keras 3 #2300

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merged 2 commits into from
Apr 12, 2024
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Update the Segmentation Tutorial Colab to be compatible with Keras 3 #2300

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Update the notebook to be compatible with Keras 3
kinarr Apr 8, 2024
2f4e5fa
Formatted notebook
kinarr Apr 9, 2024
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29 changes: 17 additions & 12 deletions site/en/tutorials/images/segmentation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,10 @@
},
"outputs": [],
"source": [
"!pip install git+https://github.com/tensorflow/examples.git"
"!pip install git+https://github.com/tensorflow/examples.git\n",
"!pip install -U keras\n",
"!pip install -q tensorflow_datasets\n",
"!pip install -q -U tensorflow-text tensorflow"
]
},
{
Expand All @@ -108,8 +111,9 @@
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds"
]
},
Expand Down Expand Up @@ -252,7 +256,7 @@
" # both use the same seed, so they'll make the same random changes.\n",
" self.augment_inputs = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n",
" self.augment_labels = tf.keras.layers.RandomFlip(mode=\"horizontal\", seed=seed)\n",
" \n",
"\n",
" def call(self, inputs, labels):\n",
" inputs = self.augment_inputs(inputs)\n",
" labels = self.augment_labels(labels)\n",
Expand Down Expand Up @@ -450,7 +454,7 @@
"source": [
"## Train the model\n",
"\n",
"Now, all that is left to do is to compile and train the model. \n",
"Now, all that is left to do is to compile and train the model.\n",
"\n",
"Since this is a multiclass classification problem, use the `tf.keras.losses.SparseCategoricalCrossentropy` loss function with the `from_logits` argument set to `True`, since the labels are scalar integers instead of vectors of scores for each pixel of every class.\n",
"\n",
Expand Down Expand Up @@ -490,7 +494,7 @@
},
"outputs": [],
"source": [
"tf.keras.utils.plot_model(model, show_shapes=True)"
"tf.keras.utils.plot_model(model, show_shapes=True, expand_nested=True, dpi=64)"
]
},
{
Expand Down Expand Up @@ -695,12 +699,14 @@
},
"outputs": [],
"source": [
"label = [0,0]\n",
"prediction = [[-3., 0], [-3, 0]] \n",
"sample_weight = [1, 10] \n",
"label = np.array([0,0])\n",
"prediction = np.array([[-3., 0], [-3, 0]])\n",
"sample_weight = [1, 10]\n",
"\n",
"loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,\n",
" reduction=tf.keras.losses.Reduction.NONE)\n",
"loss = tf.keras.losses.SparseCategoricalCrossentropy(\n",
" from_logits=True,\n",
" reduction=tf.keras.losses.Reduction.NONE\n",
")\n",
"loss(label, prediction, sample_weight).numpy()"
]
},
Expand Down Expand Up @@ -729,7 +735,7 @@
" class_weights = tf.constant([2.0, 2.0, 1.0])\n",
" class_weights = class_weights/tf.reduce_sum(class_weights)\n",
"\n",
" # Create an image of `sample_weights` by using the label at each pixel as an \n",
" # Create an image of `sample_weights` by using the label at each pixel as an\n",
" # index into the `class weights` .\n",
" sample_weights = tf.gather(class_weights, indices=tf.cast(label, tf.int32))\n",
"\n",
Expand Down Expand Up @@ -811,7 +817,6 @@
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "segmentation.ipynb",
"toc_visible": true
},
Expand Down
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