02-Cross-entropy-sparse-from_logits-flatten.ipynb

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     "text": [
      "TensorFlow version: 2.5.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"TensorFlow version:\", tf.__version__)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dropout(0.2),\n",
    "  tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.3000 - accuracy: 0.9127\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1421 - accuracy: 0.9581\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1071 - accuracy: 0.9672\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0865 - accuracy: 0.9726\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0732 - accuracy: 0.9767\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2cbcaf9daf0>"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.fit(x_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.0746 - accuracy: 0.9770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07459655404090881, 0.9769999980926514]"
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "model.evaluate(x_test,  y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "y_test[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sample = x_test[12].reshape(1,28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-10.096759  , -11.846766  ,  -4.8129873 ,  -0.20918852,\n",
       "          0.34299034,  -2.9108334 , -13.639051  ,   1.0274941 ,\n",
       "         -3.6633441 ,  10.363614  ]], dtype=float32)"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.predict(test_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([\n",
    "  model,\n",
    "  tf.keras.layers.Softmax()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.3004779e-09, 2.2598745e-10, 2.5633966e-07, 2.5598838e-05,\n",
       "        4.4466022e-05, 1.7175554e-06, 3.7644755e-11, 8.8166744e-05,\n",
       "        8.0928112e-07, 9.9983895e-01]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_model.predict(test_sample)"
   ]
  }
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