ready for 3.0.0 release (#1956)

Author: haifeng-jin

autokeras/__init__.py

@@ -51,7 +51,7 @@
 from autokeras.tuners import Hyperband
 from autokeras.tuners import RandomSearch
 
-__version__ = "3.0.0dev"
+__version__ = "3.0.0"
 
 CUSTOM_OBJECTS = {
     "CastToFloat32": CastToFloat32,

docs/ipynb/customized.ipynb

@@ -8,6 +8,7 @@
    },
    "outputs": [],
    "source": [
+    "!export KERAS_BACKEND=\"torch\"\n",
     "!pip install autokeras"
    ]
   },
@@ -135,20 +136,11 @@
     "the validation data, please refer to the Validation Data section of the\n",
     "tutorials of [Image\n",
     "Classification](/tutorial/image_classification/#validation-data), [Text\n",
-    "Classification](/tutorial/text_classification/#validation-data),\n",
+    "Classification](/tutorial/text_classification/#validation-data), [Structured\n",
+    "Data\n",
+    "Classification](/tutorial/structured_data_classification/#validation-data),\n",
     "[Multi-task and Multiple Validation](/tutorial/multi/#validation-data).\n",
     "\n",
-    "## Data Format\n",
-    "You can refer to the documentation of\n",
-    "[ImageInput](/node/#imageinput-class),\n",
-    "[TextInput](/node/#textinput-class),\n",
-    "[RegressionHead](/block/#regressionhead-class),\n",
-    "[ClassificationHead](/block/#classificationhead-class),\n",
-    "for the format of different types of data.\n",
-    "You can also refer to the Data Format section of the tutorials of\n",
-    "[Image Classification](/tutorial/image_classification/#data-format),\n",
-    "[Text Classification](/tutorial/text_classification/#data-format).\n",
-    "\n",
     "## Implement New Block\n",
     "\n",
     "You can extend the [Block](/base/#block-class)\n",
@@ -229,6 +221,7 @@
     "[ImageInput](/node/#imageinput-class),\n",
     "[Input](/node/#input-class),\n",
     "[TextInput](/node/#textinput-class).\n",
+    "[StructuredDataInput](/node/#structureddatainput-class),\n",
     "\n",
     "**Preprocessors**:\n",
     "[FeatureEngineering](/block/#featureengineering-class),\n",
@@ -239,14 +232,16 @@
     "**Blocks**:\n",
     "[ConvBlock](/block/#convblock-class),\n",
     "[DenseBlock](/block/#denseblock-class),\n",
+    "[Embedding](/block/#embedding-class),\n",
     "[Merge](/block/#merge-class),\n",
     "[ResNetBlock](/block/#resnetblock-class),\n",
     "[RNNBlock](/block/#rnnblock-class),\n",
     "[SpatialReduction](/block/#spatialreduction-class),\n",
     "[TemporalReduction](/block/#temporalreduction-class),\n",
     "[XceptionBlock](/block/#xceptionblock-class),\n",
     "[ImageBlock](/block/#imageblock-class),\n",
-    "[TextBlock](/block/#textblock-class).\n"
+    "[TextBlock](/block/#textblock-class).\n",
+    "[StructuredDataBlock](/block/#structureddatablock-class),\n"
    ]
   }
  ],

docs/ipynb/export.ipynb

@@ -8,6 +8,7 @@
    },
    "outputs": [],
    "source": [
+    "!export KERAS_BACKEND=\"torch\"\n",
     "!pip install autokeras"
    ]
   },

docs/ipynb/image_classification.ipynb

@@ -8,6 +8,7 @@
    },
    "outputs": [],
    "source": [
+    "!export KERAS_BACKEND=\"torch\"\n",
     "!pip install autokeras"
    ]
   },
@@ -19,7 +20,6 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from keras.datasets import mnist\n",
     "\n",
     "import autokeras as ak"
@@ -225,64 +225,6 @@
     "clf.fit(x_train, y_train, epochs=1)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Data Format\n",
-    "The AutoKeras ImageClassifier is quite flexible for the data format.\n",
-    "\n",
-    "For the image, it accepts data formats both with and without the channel\n",
-    "dimension. The images in the MNIST dataset do not have the channel dimension.\n",
-    "Each image is a matrix with shape (28, 28). AutoKeras also accepts images of\n",
-    "three dimensions with the channel dimension at last, e.g., (32, 32, 3), (28,\n",
-    "28, 1).\n",
-    "\n",
-    "For the classification labels, AutoKeras accepts both plain labels, i.e.\n",
-    "strings or integers, and one-hot encoded encoded labels, i.e. vectors of 0s and\n",
-    "1s.\n",
-    "\n",
-    "So if you prepare your data in the following way, the ImageClassifier should\n",
-    "still work.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape the images to have the channel dimension.\n",
-    "x_train = x_train.reshape(x_train.shape + (1,))\n",
-    "x_test = x_test.reshape(x_test.shape + (1,))\n",
-    "\n",
-    "# One-hot encode the labels.\n",
-    "eye = np.eye(10)\n",
-    "y_train = eye[y_train]\n",
-    "y_test = eye[y_test]\n",
-    "\n",
-    "print(x_train.shape)  # (60000, 28, 28, 1)\n",
-    "print(y_train.shape)  # (60000, 10)\n",
-    "print(y_train[:3])\n",
-    "# array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
-    "#        [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
-    "#        [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])\n",
-    "\n",
-    "clf = ak.ImageClassifier(overwrite=True, max_trials=1)\n",
-    "# Feed the Dataset to the classifier.\n",
-    "clf.fit(x=x_train, y=y_train, epochs=1)\n",
-    "# Predict with the best model.\n",
-    "predicted_y = clf.predict(x=x_test)\n",
-    "# Evaluate the best model with testing data.\n",
-    "print(clf.evaluate(x=x_test, y=y_test))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {

docs/ipynb/image_regression.ipynb

@@ -8,6 +8,7 @@
    },
    "outputs": [],
    "source": [
+    "!export KERAS_BACKEND=\"torch\"\n",
     "!pip install autokeras"
    ]
   },
@@ -228,57 +229,6 @@
     "reg.fit(x_train, y_train, epochs=1)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Data Format\n",
-    "The AutoKeras ImageRegressor is quite flexible for the data format.\n",
-    "\n",
-    "For the image, it accepts data formats both with and without the channel\n",
-    "dimension. The images in the MNIST dataset do not have the channel dimension.\n",
-    "Each image is a matrix with shape (28, 28). AutoKeras also accepts images of\n",
-    "three dimensions with the channel dimension at last, e.g., (32, 32, 3), (28,\n",
-    "28, 1).\n",
-    "\n",
-    "For the regression targets, it should be a vector of numerical values.\n",
-    "AutoKeras accepts numpy.ndarray.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "x_train = x_train[:100]\n",
-    "y_train = y_train[:100]\n",
-    "x_test = x_test[:100]\n",
-    "y_test = y_test[:100]\n",
-    "\n",
-    "# Reshape the images to have the channel dimension.\n",
-    "x_train = x_train.reshape(x_train.shape + (1,))\n",
-    "x_test = x_test.reshape(x_test.shape + (1,))\n",
-    "y_train = y_train.reshape(y_train.shape + (1,))\n",
-    "y_test = y_test.reshape(y_test.shape + (1,))\n",
-    "\n",
-    "print(x_train.shape)  # (60000, 28, 28, 1)\n",
-    "print(y_train.shape)  # (60000, 10)\n",
-    "\n",
-    "reg = ak.ImageRegressor(overwrite=True, max_trials=1)\n",
-    "# Feed the Dataset to the regressor.\n",
-    "reg.fit(x_train, y_train, epochs=1)\n",
-    "# Predict with the best model.\n",
-    "predicted_y = reg.predict(x_test)\n",
-    "# Evaluate the best model with testing data.\n",
-    "print(reg.evaluate(x_test, y_test))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {

docs/ipynb/multi.ipynb

@@ -8,6 +8,7 @@
    },
    "outputs": [],
    "source": [
+    "!export KERAS_BACKEND=\"torch\"\n",
     "!pip install autokeras"
    ]
   },
@@ -296,18 +297,6 @@
     "colab_type": "text"
    },
    "source": [
-    "## Data Format\n",
-    "You can refer to the documentation of\n",
-    "[ImageInput](/node/#imageinput-class),\n",
-    "[Input](/node/#input-class),\n",
-    "[TextInput](/node/#textinput-class),\n",
-    "[RegressionHead](/block/#regressionhead-class),\n",
-    "[ClassificationHead](/block/#classificationhead-class),\n",
-    "for the format of different types of data.\n",
-    "You can also refer to the Data Format section of the tutorials of\n",
-    "[Image Classification](/tutorial/image_classification/#data-format),\n",
-    "[Text Classification](/tutorial/text_classification/#data-format),\n",
-    "\n",
     "## Reference\n",
     "[AutoModel](/auto_model/#automodel-class),\n",
     "[ImageInput](/node/#imageinput-class),\n",