mnist keras CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 15s 1us/step\n"
     ]
    }
   ],
   "source": [
    "dataset = mnist.load_data('mymnist.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train , test = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , y_train = train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test , y_test = test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = X_train[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-c8ec22b3e787>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1_label = y_train[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26568dfaec8>"
      ]
     },
     "execution_count": 16,
     "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(img1 , cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1_1d = img1.reshape(28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1_1d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_1d = X_train.reshape(-1 , 28*28)\n",
    "X_test_1d = X_test.reshape(-1 , 28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_1d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train_1d.astype('float32')\n",
    "X_test = X_test_1d.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_cat = to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_cat[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=512, input_dim=28*28, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "=================================================================\n",
      "Total params: 401,920\n",
      "Trainable params: 401,920\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=256, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=128, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=32, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                4128      \n",
      "=================================================================\n",
      "Total params: 570,272\n",
      "Trainable params: 570,272\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                4128      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 570,602\n",
      "Trainable params: 570,602\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(), loss='categorical_crossentropy', \n",
    "             metrics=['accuracy']\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 24s 396us/step - loss: 2.5459 - accuracy: 0.1738\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 24s 394us/step - loss: 2.1461 - accuracy: 0.1957\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 25s 422us/step - loss: 2.1264 - accuracy: 0.1986\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 26s 429us/step - loss: 2.1418 - accuracy: 0.1968\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 23s 389us/step - loss: 2.1673 - accuracy: 0.1953\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 27s 443us/step - loss: 2.1544 - accuracy: 0.2011\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 21s 356us/step - loss: 2.1366 - accuracy: 0.2016\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 21s 349us/step - loss: 2.1061 - accuracy: 0.2063\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 22s 375us/step - loss: 2.0812 - accuracy: 0.2045\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 24s 395us/step - loss: 2.0954 - accuracy: 0.2057\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 23s 388us/step - loss: 2.0914 - accuracy: 0.2045\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 20s 341us/step - loss: 2.0681 - accuracy: 0.2059\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 20s 341us/step - loss: 2.0844 - accuracy: 0.2044\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 24s 400us/step - loss: 2.1026 - accuracy: 0.2050\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 24s 400us/step - loss: 2.1010 - accuracy: 0.2036s - loss: 2.1025 \n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 21s 353us/step - loss: 2.0830 - accuracy: 0.2065\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 22s 363us/step - loss: 2.0510 - accuracy: 0.2084\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 19s 312us/step - loss: 2.0437 - accuracy: 0.2050\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 24s 405us/step - loss: 2.0321 - accuracy: 0.2087ETA: 1s - los - ETA: 0s - l\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - ETA: 0s - loss: 2.0409 - accuracy: 0.20 - 21s 351us/step - loss: 2.0410 - accuracy: 0.2059\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(X_train, y_train_cat, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d67da1bd88>"
      ]
     },
     "execution_count": 64,
     "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[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Error when checking input: expected dense_1_input to have shape (784,) but got array with shape (28,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-66-3e09293d4776>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\mytensor\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1440\u001b[0m         \u001b[1;31m# Case 2: Symbolic tensors or Numpy array-like.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1441\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1442\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1443\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\mytensor\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    577\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m             exception_prefix='input')\n\u001b[0m\u001b[0;32m    580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\mytensor\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    143\u001b[0m                             \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m                             str(data_shape))\n\u001b[0m\u001b[0;32m    146\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected dense_1_input to have shape (784,) but got array with shape (28,)"
     ]
    }
   ],
   "source": [
    "model.predict(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img = X_test[0].reshape(28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-41b3a52197d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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