minst_keras.ipynb

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      "Epoch 1/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8657 - loss: 0.4908 - val_accuracy: 0.9500 - val_loss: 0.1710\n",
      "Epoch 2/10\n"
     ]
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     "text": [
      "C:\\Program Files\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_3.11.2544.0_x64__qbz5n2kfra8p0\\Lib\\contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
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     "text": [
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 580us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.9500 - val_loss: 0.1710\n",
      "Epoch 3/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9553 - loss: 0.1548 - val_accuracy: 0.9623 - val_loss: 0.1249\n",
      "Epoch 4/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 598us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.9623 - val_loss: 0.1249\n",
      "Epoch 5/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9699 - loss: 0.1005 - val_accuracy: 0.9715 - val_loss: 0.1004\n",
      "Epoch 6/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.9715 - val_loss: 0.1004\n",
      "Epoch 7/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9779 - loss: 0.0745 - val_accuracy: 0.9738 - val_loss: 0.0915\n",
      "Epoch 8/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.9738 - val_loss: 0.0915\n",
      "Epoch 9/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9828 - loss: 0.0601 - val_accuracy: 0.9750 - val_loss: 0.0911\n",
      "Epoch 10/10\n",
      "\u001b[1m1000/1000\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261us/step - accuracy: 0.0000e+00 - loss: 0.0000e+00 - val_accuracy: 0.9750 - val_loss: 0.0911\n"
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     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1de97f9c890>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "import tensorflow as tf \n",
    "import numpy as np \n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)\n",
    "\n",
    "model.compile(\n",
    "    loss=scce,\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "x_train = np.asarray(x_train.reshape(-1, 784) / 255.0)\n",
    "x_test = np.asarray(x_test.reshape(-1, 784) / 255.0)\n",
    "\n",
    "model.fit(x_train, y_train, epochs=10, validation_split=0.2, steps_per_epoch=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 728us/step - accuracy: 0.9722 - loss: 0.0954\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(\n",
    "    x=x_test,\n",
    "    y=y_test,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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