Keras 3 imbalanced classification

Marked as tf only for some reason, but this is actually all backends
mattdangerw · Nov 11, 2023 · 17632d4 · 17632d4
1 parent ea7f24e
commit 17632d4
Show file tree

Hide file tree

Showing 3 changed files with 93 additions and 83 deletions.
diff --git a/examples/structured_data/imbalanced_classification.py b/examples/structured_data/imbalanced_classification.py
@@ -85,13 +85,12 @@
 ## Build a binary classification model
 """
 
-from tensorflow import keras
+import keras
 
 model = keras.Sequential(
     [
-        keras.layers.Dense(
-            256, activation="relu", input_shape=(train_features.shape[-1],)
-        ),
+        keras.Input(shape=train_features.shape[1:]),
+        keras.layers.Dense(256, activation="relu"),
         keras.layers.Dense(256, activation="relu"),
         keras.layers.Dropout(0.3),
         keras.layers.Dense(256, activation="relu"),
@@ -118,7 +117,7 @@
     optimizer=keras.optimizers.Adam(1e-2), loss="binary_crossentropy", metrics=metrics
 )
 
-callbacks = [keras.callbacks.ModelCheckpoint("fraud_model_at_epoch_{epoch}.h5")]
+callbacks = [keras.callbacks.ModelCheckpoint("fraud_model_at_epoch_{epoch}.keras")]
 class_weight = {0: weight_for_0, 1: weight_for_1}
 
 model.fit(

diff --git a/examples/structured_data/ipynb/imbalanced_classification.ipynb b/examples/structured_data/ipynb/imbalanced_classification.ipynb
@@ -67,7 +67,7 @@
     "features = np.array(all_features, dtype=\"float32\")\n",
     "targets = np.array(all_targets, dtype=\"uint8\")\n",
     "print(\"features.shape:\", features.shape)\n",
-    "print(\"targets.shape:\", targets.shape)\n"
+    "print(\"targets.shape:\", targets.shape)"
    ]
   },
   {
@@ -94,7 +94,7 @@
     "val_targets = targets[-num_val_samples:]\n",
     "\n",
     "print(\"Number of training samples:\", len(train_features))\n",
-    "print(\"Number of validation samples:\", len(val_features))\n"
+    "print(\"Number of validation samples:\", len(val_features))"
    ]
   },
   {
@@ -122,7 +122,7 @@
     ")\n",
     "\n",
     "weight_for_0 = 1.0 / counts[0]\n",
-    "weight_for_1 = 1.0 / counts[1]\n"
+    "weight_for_1 = 1.0 / counts[1]"
    ]
   },
   {
@@ -147,7 +147,7 @@
     "val_features -= mean\n",
     "std = np.std(train_features, axis=0)\n",
     "train_features /= std\n",
-    "val_features /= std\n"
+    "val_features /= std"
    ]
   },
   {
@@ -167,21 +167,20 @@
    },
    "outputs": [],
    "source": [
-    "from tensorflow import keras\n",
+    "import keras\n",
     "\n",
     "model = keras.Sequential(\n",
     "    [\n",
-    "        keras.layers.Dense(\n",
-    "            256, activation=\"relu\", input_shape=(train_features.shape[-1],)\n",
-    "        ),\n",
+    "        keras.Input(shape=train_features.shape[1:]),\n",
+    "        keras.layers.Dense(256, activation=\"relu\"),\n",
     "        keras.layers.Dense(256, activation=\"relu\"),\n",
     "        keras.layers.Dropout(0.3),\n",
     "        keras.layers.Dense(256, activation=\"relu\"),\n",
     "        keras.layers.Dropout(0.3),\n",
     "        keras.layers.Dense(1, activation=\"sigmoid\"),\n",
     "    ]\n",
     ")\n",
-    "model.summary()\n"
+    "model.summary()"
    ]
   },
   {
@@ -214,7 +213,7 @@
     "    optimizer=keras.optimizers.Adam(1e-2), loss=\"binary_crossentropy\", metrics=metrics\n",
     ")\n",
     "\n",
-    "callbacks = [keras.callbacks.ModelCheckpoint(\"fraud_model_at_epoch_{epoch}.h5\")]\n",
+    "callbacks = [keras.callbacks.ModelCheckpoint(\"fraud_model_at_epoch_{epoch}.keras\")]\n",
     "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
     "\n",
     "model.fit(\n",
@@ -226,7 +225,7 @@
     "    callbacks=callbacks,\n",
     "    validation_data=(val_features, val_targets),\n",
     "    class_weight=class_weight,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -252,7 +251,7 @@
     "\n",
     "| Trained Model | Demo |\n",
     "| :--: | :--: |\n",
-    "| [![Generic badge](https://img.shields.io/badge/🤗%20Model-Imbalanced%20Classification-black.svg)](https://huggingface.co/keras-io/imbalanced_classification) | [![Generic badge](https://img.shields.io/badge/🤗%20Spaces-Imbalanced%20Classification-black.svg)](https://huggingface.co/spaces/keras-io/Credit_Card_Fraud_Detection) |\n"
+    "| [![Generic badge](https://img.shields.io/badge/\ud83e\udd17%20Model-Imbalanced%20Classification-black.svg)](https://huggingface.co/keras-io/imbalanced_classification) | [![Generic badge](https://img.shields.io/badge/\ud83e\udd17%20Spaces-Imbalanced%20Classification-black.svg)](https://huggingface.co/spaces/keras-io/Credit_Card_Fraud_Detection) |"
    ]
   }
  ],