Use stable keras and keras-hub, bug fixes

Author: mattdangerw

chapter02_mathematical-building-blocks.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
+    "!pip install keras keras-hub --upgrade -q"
    ]
   },
   {
@@ -430,7 +430,7 @@
     "\n",
     "digit = train_images[4]\n",
     "plt.imshow(digit, cmap=plt.cm.binary)\n",
-    "plt.savefig(\"The-fourth-sample-in-our-dataset.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {

chapter03_introduction-to-ml-frameworks.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
+    "!pip install keras keras-hub --upgrade -q"
    ]
   },
   {
@@ -159,7 +159,7 @@
     "colab_type": "text"
    },
    "source": [
-    "###### Tensor assignment and the Variable class"
+    "###### Tensor assignment and the `Variable` class"
    ]
   },
   {
@@ -413,7 +413,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plt.scatter(inputs[:, 0], inputs[:, 1], c=targets[:, 0])\n",
-    "plt.savefig(\"linear_model_inputs.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -499,7 +499,7 @@
    "source": [
     "predictions = model(inputs, W, b)\n",
     "plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\n",
-    "plt.savefig(\"linear_model_predictions.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -684,7 +684,7 @@
     "c = torch.sqrt(a)\n",
     "d = b + c\n",
     "e = torch.matmul(a, b)\n",
-    "f = torch.cat((a, b), axis=0)"
+    "f = torch.cat((a, b), dim=0)"
    ]
   },
   {
@@ -825,7 +825,7 @@
     "colab_type": "text"
    },
    "source": [
-    "##### Packaging state and computation with Modules"
+    "##### Packaging state and computation with the `Module` class"
    ]
   },
   {
@@ -914,7 +914,7 @@
    },
    "outputs": [],
    "source": [
-    "compiled_model = model.compile()"
+    "compiled_model = torch.compile(model)"
    ]
   },
   {
@@ -1006,7 +1006,7 @@
     "colab_type": "text"
    },
    "source": [
-    "#### Random tensors"
+    "#### Random number generation in JAX"
    ]
   },
   {
@@ -1060,7 +1060,7 @@
    "source": [
     "import jax\n",
     "\n",
-    "seed_key = jax.random.PRNGKey(1337)"
+    "seed_key = jax.random.key(1337)"
    ]
   },
   {
@@ -1071,7 +1071,7 @@
    },
    "outputs": [],
    "source": [
-    "seed_key = jax.random.PRNGKey(0)\n",
+    "seed_key = jax.random.key(0)\n",
     "jax.random.normal(seed_key, shape=(3,))"
    ]
   },
@@ -1083,7 +1083,7 @@
    },
    "outputs": [],
    "source": [
-    "seed_key = jax.random.PRNGKey(123)\n",
+    "seed_key = jax.random.key(123)\n",
     "jax.random.normal(seed_key, shape=(3,))"
    ]
   },
@@ -1106,7 +1106,7 @@
    },
    "outputs": [],
    "source": [
-    "seed_key = jax.random.PRNGKey(123)\n",
+    "seed_key = jax.random.key(123)\n",
     "jax.random.normal(seed_key, shape=(3,))"
    ]
   },
@@ -1353,6 +1353,8 @@
    },
    "outputs": [],
    "source": [
+    "learning_rate = 0.1\n",
+    "\n",
     "@jax.jit\n",
     "def training_step(inputs, targets, W, b):\n",
     "    loss, grads = grad_fn((W, b), inputs, targets)\n",

chapter04_classification-and-regression.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
+    "!pip install keras keras-hub --upgrade -q"
    ]
   },
   {
@@ -172,8 +172,8 @@
    },
    "outputs": [],
    "source": [
-    "y_train = np.asarray(train_labels).astype(\"float32\")\n",
-    "y_test = np.asarray(test_labels).astype(\"float32\")"
+    "y_train = train_labels.astype(\"float32\")\n",
+    "y_test = test_labels.astype(\"float32\")"
    ]
   },
   {
@@ -310,7 +310,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"imdb_loss_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -331,7 +331,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Accuracy\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"imdb_accuracy_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -654,7 +654,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"reuters_loss_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -675,7 +675,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Accuracy\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"reuters_accuracy_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -696,7 +696,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Top-3 accuracy\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"reuters_top_3_accuracy_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -750,7 +750,7 @@
     "import copy\n",
     "test_labels_copy = copy.copy(test_labels)\n",
     "np.random.shuffle(test_labels_copy)\n",
-    "hits_array = np.array(test_labels)\n",
+    "hits_array = np.array(test_labels == test_labels_copy)\n",
     "hits_array.mean()"
    ]
   },
@@ -824,8 +824,8 @@
    },
    "outputs": [],
    "source": [
-    "y_train = np.array(train_labels)\n",
-    "y_test = np.array(test_labels)"
+    "y_train = train_labels\n",
+    "y_test = test_labels"
    ]
   },
   {
@@ -1163,7 +1163,7 @@
     "plt.plot(epochs, average_mae_history)\n",
     "plt.xlabel(\"Epochs\")\n",
     "plt.ylabel(\"Validation MAE\")\n",
-    "plt.savefig(\"california_housing_validation_mae_plot.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -1179,7 +1179,7 @@
     "plt.plot(epochs, truncated_mae_history)\n",
     "plt.xlabel(\"Epochs\")\n",
     "plt.ylabel(\"Validation MAE\")\n",
-    "plt.savefig(\"california_housing_validation_mae_plot_zoomed.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -1225,7 +1225,7 @@
    },
    "outputs": [],
    "source": [
-    "predictions = model.predict(test_data)\n",
+    "predictions = model.predict(x_test)\n",
     "predictions[0]"
    ]
   },

chapter05_fundamentals-of-ml.ipynb

@@ -17,7 +17,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install keras-nightly keras-hub-nightly --upgrade -q"
+    "!pip install keras keras-hub --upgrade -q"
    ]
   },
   {
@@ -175,7 +175,7 @@
     "plt.xticks(epochs)\n",
     "plt.ylabel(\"Accuracy\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"mnist_with_added_noise_channels_or_zeros_channels.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -445,7 +445,7 @@
     "plt.xlabel(\"Epochs\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"effect_of_insufficient_model_capacity_on_val_loss.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -485,14 +485,14 @@
    },
    "outputs": [],
    "source": [
-    "val_loss = history_small_model.history[\"val_loss\"]\n",
+    "val_loss = history_large_model.history[\"val_loss\"]\n",
     "epochs = range(1, 21)\n",
     "plt.plot(epochs, val_loss, \"b-\", label=\"Validation loss\")\n",
     "plt.title(\"Validation loss for a model with appropriate capacity\")\n",
     "plt.xlabel(\"Epochs\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"effect_of_correct_model_capacity_on_val_loss.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -507,6 +507,7 @@
     "    [\n",
     "        layers.Dense(2048, activation=\"relu\"),\n",
     "        layers.Dense(2048, activation=\"relu\"),\n",
+    "        layers.Dense(2048, activation=\"relu\"),\n",
     "        layers.Dense(10, activation=\"softmax\"),\n",
     "    ]\n",
     ")\n",
@@ -519,7 +520,7 @@
     "    train_images,\n",
     "    train_labels,\n",
     "    epochs=20,\n",
-    "    batch_size=128,\n",
+    "    batch_size=32,\n",
     "    validation_split=0.2,\n",
     ")"
    ]
@@ -539,7 +540,7 @@
     "plt.xlabel(\"Epochs\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.legend()\n",
-    "plt.savefig(\"effect_of_excessive_model_capacity_on_val_loss.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -694,7 +695,7 @@
     "plt.ylabel(\"Loss\")\n",
     "plt.xticks(epochs)\n",
     "plt.legend()\n",
-    "plt.savefig(\"original_model_vs_smaller_model_imdb.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -735,7 +736,7 @@
    "outputs": [],
    "source": [
     "original_val_loss = history_original.history[\"val_loss\"]\n",
-    "larger_model_val_loss = history_smaller_model.history[\"val_loss\"]\n",
+    "larger_model_val_loss = history_larger_model.history[\"val_loss\"]\n",
     "epochs = range(1, 21)\n",
     "plt.plot(\n",
     "    epochs,\n",
@@ -754,7 +755,7 @@
     "plt.ylabel(\"Loss\")\n",
     "plt.xticks(epochs)\n",
     "plt.legend()\n",
-    "plt.savefig(\"original_model_vs_larger_model_imdb.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -827,7 +828,7 @@
     "plt.ylabel(\"Loss\")\n",
     "plt.xticks(epochs)\n",
     "plt.legend()\n",
-    "plt.savefig(\"original_model_vs_l2_regularized_model_imdb.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {
@@ -914,7 +915,7 @@
     "plt.ylabel(\"Loss\")\n",
     "plt.xticks(epochs)\n",
     "plt.legend()\n",
-    "plt.savefig(\"original_model_vs_dropout_regularized_model_imdb.png\", dpi=300)"
+    "plt.show()"
    ]
   },
   {