Update the siamese contrastive example for Keras 3.

examples/vision/ipynb/siamese_contrastive.ipynb

@@ -51,9 +51,8 @@
    "source": [
     "import random\n",
     "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras import layers\n",
+    "import keras\n",
+    "from keras import ops\n",
     "import matplotlib.pyplot as plt"
    ]
   },
@@ -350,14 +349,13 @@
     "    # Plot the images\n",
     "    fig, axes = plt.subplots(num_row, num_col, figsize=(5, 5))\n",
     "    for i in range(to_show):\n",
-    "\n",
     "        # If the number of rows is 1, the axes array is one-dimensional\n",
     "        if num_row == 1:\n",
     "            ax = axes[i % num_col]\n",
     "        else:\n",
     "            ax = axes[i // num_col, i % num_col]\n",
     "\n",
-    "        ax.imshow(tf.concat([pairs[i][0], pairs[i][1]], axis=1), cmap=\"gray\")\n",
+    "        ax.imshow(ops.concatenate([pairs[i][0], pairs[i][1]], axis=1), cmap=\"gray\")\n",
     "        ax.set_axis_off()\n",
     "        if test:\n",
     "            ax.set_title(\"True: {} | Pred: {:.5f}\".format(labels[i], predictions[i][0]))\n",
@@ -453,6 +451,7 @@
    },
    "outputs": [],
    "source": [
+    "\n",
     "# Provided two tensors t1 and t2\n",
     "# Euclidean distance = sqrt(sum(square(t1-t2)))\n",
     "def euclidean_distance(vects):\n",
@@ -467,35 +466,37 @@
     "    \"\"\"\n",
     "\n",
     "    x, y = vects\n",
-    "    sum_square = tf.math.reduce_sum(tf.math.square(x - y), axis=1, keepdims=True)\n",
-    "    return tf.math.sqrt(tf.math.maximum(sum_square, tf.keras.backend.epsilon()))\n",
+    "    sum_square = ops.sum(ops.square(x - y), axis=1, keepdims=True)\n",
+    "    return ops.sqrt(ops.maximum(sum_square, keras.backend.epsilon()))\n",
     "\n",
     "\n",
-    "input = layers.Input((28, 28, 1))\n",
-    "x = tf.keras.layers.BatchNormalization()(input)\n",
-    "x = layers.Conv2D(4, (5, 5), activation=\"tanh\")(x)\n",
-    "x = layers.AveragePooling2D(pool_size=(2, 2))(x)\n",
-    "x = layers.Conv2D(16, (5, 5), activation=\"tanh\")(x)\n",
-    "x = layers.AveragePooling2D(pool_size=(2, 2))(x)\n",
-    "x = layers.Flatten()(x)\n",
+    "input = keras.layers.Input((28, 28, 1))\n",
+    "x = keras.layers.BatchNormalization()(input)\n",
+    "x = keras.layers.Conv2D(4, (5, 5), activation=\"tanh\")(x)\n",
+    "x = keras.layers.AveragePooling2D(pool_size=(2, 2))(x)\n",
+    "x = keras.layers.Conv2D(16, (5, 5), activation=\"tanh\")(x)\n",
+    "x = keras.layers.AveragePooling2D(pool_size=(2, 2))(x)\n",
+    "x = keras.layers.Flatten()(x)\n",
     "\n",
-    "x = tf.keras.layers.BatchNormalization()(x)\n",
-    "x = layers.Dense(10, activation=\"tanh\")(x)\n",
+    "x = keras.layers.BatchNormalization()(x)\n",
+    "x = keras.layers.Dense(10, activation=\"tanh\")(x)\n",
     "embedding_network = keras.Model(input, x)\n",
     "\n",
     "\n",
-    "input_1 = layers.Input((28, 28, 1))\n",
-    "input_2 = layers.Input((28, 28, 1))\n",
+    "input_1 = keras.layers.Input((28, 28, 1))\n",
+    "input_2 = keras.layers.Input((28, 28, 1))\n",
     "\n",
     "# As mentioned above, Siamese Network share weights between\n",
     "# tower networks (sister networks). To allow this, we will use\n",
     "# same embedding network for both tower networks.\n",
     "tower_1 = embedding_network(input_1)\n",
     "tower_2 = embedding_network(input_2)\n",
     "\n",
-    "merge_layer = layers.Lambda(euclidean_distance)([tower_1, tower_2])\n",
-    "normal_layer = tf.keras.layers.BatchNormalization()(merge_layer)\n",
-    "output_layer = layers.Dense(1, activation=\"sigmoid\")(normal_layer)\n",
+    "merge_layer = keras.layers.Lambda(euclidean_distance, output_shape=(1,))(\n",
+    "    [tower_1, tower_2]\n",
+    ")\n",
+    "normal_layer = keras.layers.BatchNormalization()(merge_layer)\n",
+    "output_layer = keras.layers.Dense(1, activation=\"sigmoid\")(normal_layer)\n",
     "siamese = keras.Model(inputs=[input_1, input_2], outputs=output_layer)\n",
     ""
    ]
@@ -543,11 +544,9 @@
     "            A tensor containing contrastive loss as floating point value.\n",
     "        \"\"\"\n",
     "\n",
-    "        square_pred = tf.math.square(y_pred)\n",
-    "        margin_square = tf.math.square(tf.math.maximum(margin - (y_pred), 0))\n",
-    "        return tf.math.reduce_mean(\n",
-    "            (1 - y_true) * square_pred + (y_true) * margin_square\n",
-    "        )\n",
+    "        square_pred = ops.square(y_pred)\n",
+    "        margin_square = ops.square(ops.maximum(margin - (y_pred), 0))\n",
+    "        return ops.mean((1 - y_true) * square_pred + (y_true) * margin_square)\n",
     "\n",
     "    return contrastive_loss\n",
     ""