Update binary_logistic_regression_tf_with_hidden_layers.ipynb

binary_logistic_regression_tf_with_hidden_layers.ipynb

@@ -44,8 +44,8 @@
     "from sklearn.datasets import make_classification\n",
     "from sklearn.model_selection import train_test_split\n",
     "import tensorflow as tf\n",
-    "import tensorflow.keras as keras\n",
-    "import tensorflow.keras.backend as K\n",
+    "from tensorflow import keras\n",
+    "\n",
     "\n",
     "print(\n",
     "    \"TF version\",\n",
@@ -229,8 +229,7 @@
     "\n",
     "model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
     "\n",
-    "tw = np.sum([K.count_params(w) for w in model.trainable_weights])\n",
-    "print(\"\\ntrainable_weights:\", tw, \"\\n\")"
+    "model.summary()"
    ]
   },
   {
@@ -249,7 +248,7 @@
    "outputs": [],
    "source": [
     "model.fit(\n",
-    "    X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=verbose\n",
+    "    X_train, Y_train[:, None], epochs=epochs, batch_size=batch_size, verbose=verbose\n",
     ")"
    ]
   },
@@ -279,9 +278,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = model.evaluate(X_train, Y_train, batch_size=M_train, verbose=False)\n",
+    "results = model.evaluate(X_train, Y_train[:, None], batch_size=M_train, verbose=False)\n",
     "Y_train_pred = model.predict(X_train)\n",
-    "predict_class(Y_train_pred)"
+    "predict_class(Y_train_pred[:, None])"
    ]
   },
   {
@@ -333,7 +332,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = model.evaluate(X_test, Y_test, batch_size=M_test, verbose=False)\n",
+    "results = model.evaluate(X_test, Y_test[:,None], batch_size=M_test, verbose=False)\n",
     "Y_test_pred = model.predict(X_test)\n",
     "predict_class(Y_test_pred)"
    ]
@@ -467,7 +466,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,