fashion_prediction.ipynb

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  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b74144ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d6301cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_data=tf.keras.datasets.fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5acad401",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=fashion_data.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1f4f074",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 4,
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   ],
   "source": [
    "x_train.shape"
   ]
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   "id": "a12e60a1",
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       "        0.        , 0.        , 0.15686275, 0.23921569, 0.17254902,\n",
       "        0.28235294, 0.16078431, 0.1372549 , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train=x_train/255\n",
    "x_test=x_test/255\n",
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6eff810a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "    tf.keras.layers.Dense(units=128,activation='relu'),\n",
    "    tf.keras.layers.Dense(units=128,activation='relu'),\n",
    "#     tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(units=10,activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8fa712f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "758f3b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4856 - accuracy: 0.8262\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3629 - accuracy: 0.8679\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3273 - accuracy: 0.8799\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3063 - accuracy: 0.8860\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2864 - accuracy: 0.8925\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2732 - accuracy: 0.8978\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2597 - accuracy: 0.9011\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2507 - accuracy: 0.9054\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2383 - accuracy: 0.9100\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2320 - accuracy: 0.9130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x259aa6d8e50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0828ac72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 965us/step - loss: 0.3443 - accuracy: 0.8801\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3443104922771454, 0.8801000118255615]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e1bb986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 17ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[4.5772999e-10, 1.0133871e-07, 8.5984458e-10, 6.0684235e-11,\n",
       "        5.3268865e-09, 1.2909762e-04, 6.7801991e-09, 2.1378873e-03,\n",
       "        7.4544748e-10, 9.9773288e-01]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model.predict(x_test[0].reshape(1,(28*28)))\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0507fae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d475139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d3b52a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bb71d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.5772999e-10, 1.0133871e-07, 8.5984292e-10, ..., 2.1378864e-03,\n",
       "        7.4544609e-10, 9.9773288e-01],\n",
       "       [2.5367422e-05, 6.2353629e-12, 9.9035817e-01, ..., 4.2171385e-14,\n",
       "        2.3153181e-11, 2.7194219e-17],\n",
       "       [5.3571036e-14, 1.0000000e+00, 6.4281090e-21, ..., 4.6315042e-32,\n",
       "        4.8731833e-16, 1.4744017e-30],\n",
       "       ...,\n",
       "       [8.5811439e-04, 1.2149998e-12, 1.5226891e-06, ..., 6.4673134e-07,\n",
       "        9.9905843e-01, 1.0317832e-11],\n",
       "       [1.2864065e-10, 1.0000000e+00, 1.5409400e-14, ..., 9.7152683e-19,\n",
       "        2.8168223e-09, 2.9117856e-17],\n",
       "       [2.5812521e-06, 2.4514668e-10, 4.7397272e-08, ..., 1.8172518e-04,\n",
       "        2.1550444e-05, 2.0853481e-06]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5615d6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output=np.zeros(predictions.shape[0])\n",
    "for i in range(len(predictions)):\n",
    "    output[i]=np.max(predictions[i])\n",
    "output    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d40a24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 30ms/step\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(model.predict(x_test[4].reshape(1,(28*28)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4e0e23f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "59dc8e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x259c8331520>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "802213dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}