transfer-learning.ipynb

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      "C:\\Users\\aditya\\AppData\\Local\\Continuum\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.layers import Dense,GlobalAveragePooling2D\n",
    "from keras.applications import MobileNet\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.mobilenet import preprocess_input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam"
   ]
  },
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      "C:\\Users\\aditya\\AppData\\Local\\Continuum\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\applications\\mobilenet.py:224: UserWarning: MobileNet shape is undefined. Weights for input shape (224, 224) will be loaded.\n",
      "  warnings.warn('MobileNet shape is undefined.'\n"
     ]
    }
   ],
   "source": [
    "base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
    "\n",
    "x=base_model.output\n",
    "x=GlobalAveragePooling2D()(x)\n",
    "x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
    "x=Dense(1024,activation='relu')(x) #dense layer 2\n",
    "x=Dense(512,activation='relu')(x) #dense layer 3\n",
    "preds=Dense(3,activation='softmax')(x) #final layer with softmax activation"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "model=Model(inputs=base_model.input,outputs=preds)\n",
    "#specify the inputs\n",
    "#specify the outputs\n",
    "#now a model has been created based on our architecture"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "for layer in model.layers[:20]:\n",
    "    layer.trainable=False\n",
    "for layer in model.layers[20:]:\n",
    "    layer.trainable=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "Found 197 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies\n",
    "\n",
    "train_generator=train_datagen.flow_from_directory('./train/', # this is where you specify the path to the main data folder\n",
    "                                                 target_size=(224,224),\n",
    "                                                 color_mode='rgb',\n",
    "                                                 batch_size=32,\n",
    "                                                 class_mode='categorical',\n",
    "                                                 shuffle=True)"
   ]
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   "cell_type": "code",
   "execution_count": 33,
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     "text": [
      "Epoch 1/5\n",
      "24/24 [==============================] - 19s 802ms/step - loss: 0.6303 - acc: 0.9114\n",
      "Epoch 2/5\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5115 - acc: 0.7967\n",
      "Epoch 3/5\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2509 - acc: 0.9240\n",
      "Epoch 4/5\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2698 - acc: 0.9375\n",
      "Epoch 5/5\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2309 - acc: 0.9427\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2740d9b3f28>"
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     },
     "execution_count": 33,
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     "output_type": "execute_result"
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   ],
   "source": [
    "model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "# Adam optimizer\n",
    "# loss function will be categorical cross entropy\n",
    "# evaluation metric will be accuracy\n",
    "\n",
    "step_size_train=train_generator.n//train_generator.batch_size\n",
    "model.fit_generator(generator=train_generator,\n",
    "                   steps_per_epoch=step_size_train,\n",
    "                   epochs=5)"
   ]
  }
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