3.7_Some_exercises_with_keras.ipynb

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   "source": [
    "# Some exercises with keras"
   ]
  },
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   "source": [
    "### Exercice 1:\n",
    "CIFAR10 small image classification\n",
    "\n",
    "The dataset consist of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images. "
   ]
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import MINST data\n",
    "from keras.datasets import cifar10\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation \n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### Exercice 2\n",
    "The learning rate of the optimizer can be modified with the following function:"
   ]
  },
  {
   "cell_type": "raw",
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   "source": [
    "adam.lr.assign(learning_rate)"
   ]
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   "source": [
    "Modify the code in order to decrease the learning_rate value every 10 Epochs. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercice 3"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "First of all, let's play a little bit with http://playground.tensorflow.org/"
   ]
  },
  {
   "cell_type": "raw",
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   "source": [
    "Then create a mulitlayer perceptron with the following data and architecture you think is the best:"
   ]
  },
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   "execution_count": 2,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# the data, shuffled and split between train and test sets\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "X, y = make_moons(n_samples = 1000,noise=0.3, random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)\n",
    "print X_train.shape"
   ]
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   "execution_count": null,
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