{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"41b8c89d-194b-07a1-277f-3fb51c611fd9"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \nfrom __future__ import division\n\nimport six\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport glob\nimport random\n\nnp.random.seed(2016)\nrandom.seed(2016)\n\nfrom keras.models import Model\nfrom keras.models import Sequential\nfrom keras.optimizers import SGD\nfrom keras.layers import Input, Activation, merge, Dense, Flatten\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D, AveragePooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.regularizers import l2\nfrom keras import backend as K\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\nimport matplotlib.pylab as plt\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom glob import glob\nimport os\n\nimport theano\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0cde25a7-7788-234f-5645-5efe45bdeafe"},"outputs":[],"source":"%%javascript\nIPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1e1d1ad4-7512-834e-99ea-f2990d21e1f2"},"outputs":[],"source":"model = Sequential()\nmodel.add(Dense(128,input_dim=200))\nmodel.add(Activation('sigmoid'))\nmodel.add(Dense(256))\nmodel.add(Activation('sigmoid'))\nmodel.add(Dense(5))\nmodel.add(Activation('softmax'))\nmodel.summary()\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"126fd0d9-013b-0b7d-6162-9b31ca2df61d"},"outputs":[],"source":"from keras.optimizers import SGD,Adam,RMSprop,Adagrad\nsgd = SGD(lr=0.01,momentum=0.0,decay=0.0,nesterov=False)\nmodel.compile(loss='categorical_crossentropy',optimizer=sgd)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"df553d2f-141b-02d8-53dc-e7a17e0ae5f3"},"outputs":[],"source":"from __future__ import division, print_function, absolute_import\nimport tflearn\nfrom tflearn.data_utils import shuffle\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.estimator import regression\nfrom tflearn.data_preprocessing import ImagePreprocessing\nfrom tflearn.data_augmentation import ImageAugmentation\nimport pickle\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"23c85132-09ae-d8bb-e06c-f34f71060765"},"outputs":[],"source":"# Load the data set\nX, Y, X_test, Y_test = pickle.load(open(\"../input/test/177\", \"jpg\"))"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}