{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.Sequential()\n",
    "model.add(layers.Dense(64, activation=\"relu\", input_shape=(784,)))\n",
    "model.add(layers.Dense(64, activation=\"relu\"))\n",
    "model.add(layers.Dense(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.Sequential()\n",
    "model.add(keras.Input(shape=(784,)))\n",
    "model.add(layers.Dense(64, activation=\"relu\"))\n",
    "model.add(layers.Dense(64, activation=\"relu\"))\n",
    "model.add(layers.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### functional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(784,))\n",
    "x1 = layers.Dense(64, activation=\"relu\")(inputs)\n",
    "x2 = layers.Dense(64, activation=\"relu\")(x1)\n",
    "outputs = layers.Dense(10)(x2)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs, name=\"mnist_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.utils.plot_model(model, \"my_first_model.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "more: \n",
    "    https://keras.io/guides/functional_api/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(32, 32, 3), name=\"img\")\n",
    "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs)\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\")(x)\n",
    "block_1_output = layers.MaxPooling2D(3)(x)\n",
    "\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(block_1_output)\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "block_2_output = layers.add([x, block_1_output])\n",
    "\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(block_2_output)\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
    "block_3_output = layers.add([x, block_2_output])\n",
    "\n",
    "x = layers.Conv2D(64, 3, activation=\"relu\")(block_3_output)\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "x = layers.Dense(256, activation=\"relu\")(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(10)(x)\n",
    "\n",
    "model = keras.Model(inputs, outputs, name=\"toy_resnet\")\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "\n",
    "x_train = x_train.astype(\"float32\") / 255.0\n",
    "x_test = x_test.astype(\"float32\") / 255.0\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.RMSprop(1e-3),\n",
    "    loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[\"acc\"],\n",
    ")\n",
    "# We restrict the data to the first 1000 samples so as to limit execution time\n",
    "# on Colab. Try to train on the entire dataset until convergence!\n",
    "model.fit(x_train[:1000], y_train[:1000], batch_size=64, epochs=1, validation_split=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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