nmelone
7b30c71b53
Added Evaluation, Preprocessing, Training, Transformation folders. Preprocessing is just a rework of the folder for the new structure of the old preprocessing folder. Training and Transformation are the old project file broken up into two parts and restructured. Evaluation is for evaluating the predictive power of the model.
545 lines
19 KiB
Plaintext
545 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import os\n",
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"np.random.seed(123)\n",
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"from six.moves import cPickle\n",
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"\n",
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"from keras import backend as K\n",
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"from keras.models import Model\n",
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"from keras.layers import Input, Dense, Flatten\n",
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"from keras.layers import LSTM\n",
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"from keras.layers import TimeDistributed\n",
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"from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
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"from keras.optimizers import Adam\n",
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"\n",
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"from prednet import PredNet\n",
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"from data_utils import SequenceGenerator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"WEIGHTS_DIR = './weights/'\n",
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"DATA_DIR = '../data/'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"save_model = True # if weights will be saved\n",
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"weights_file = os.path.join(WEIGHTS_DIR, 'prednet_weather_weights.hdf5') # where weights will be saved\n",
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"json_file = os.path.join(WEIGHTS_DIR, 'prednet_weather_model.json')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data files\n",
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"train_file = os.path.join(DATA_DIR, 'x_train.hkl')\n",
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"train_sources = os.path.join(DATA_DIR, 'sources_train.hkl')\n",
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"val_file = os.path.join(DATA_DIR, 'x_val.hkl')\n",
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"val_sources = os.path.join(DATA_DIR, 'sources_val.hkl')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Training parameters\n",
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"nb_epoch = 150\n",
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"batch_size = 24\n",
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"samples_per_epoch = 500\n",
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"N_seq_val = 140 # number of sequences to use for validation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Model parameters\n",
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"n_channels, im_height, im_width = (7, 20, 40)\n",
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"input_shape = (n_channels, im_height, im_width) if K.image_data_format() == 'channels_first' else (im_height, im_width, n_channels)\n",
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"stack_sizes = (n_channels, 48, 96)\n",
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"R_stack_sizes = stack_sizes\n",
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"A_filt_sizes = (3, 3)\n",
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"Ahat_filt_sizes = (3, 3, 3)\n",
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"R_filt_sizes = (3, 3, 3)\n",
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"layer_loss_weights = np.array([1., 0., 0.]) # weighting for each layer in final loss; \"L_0\" model: [1, 0, 0, 0], \"L_all\": [1, 0.1, 0.1, 0.1]\n",
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"layer_loss_weights = np.expand_dims(layer_loss_weights, 1)\n",
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"nt = 24 # number of timesteps used for sequences in training\n",
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"time_loss_weights = 1./ (nt - 1) * np.ones((nt,1)) # equally weight all timesteps except the first\n",
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"time_loss_weights[0] = 0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"prednet = PredNet(stack_sizes, R_stack_sizes,\n",
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" A_filt_sizes, Ahat_filt_sizes, R_filt_sizes,\n",
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" output_mode='error', return_sequences=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"inputs = Input(shape=(nt,) + input_shape)\n",
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"errors = prednet(inputs) # errors will be (batch_size, nt, nb_layers)\n",
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"errors_by_time = TimeDistributed(Dense(1, trainable=False), weights=[layer_loss_weights, np.zeros(1)], trainable=False)(errors) # calculate weighted error by layer\n",
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"errors_by_time = Flatten()(errors_by_time) # will be (batch_size, nt)\n",
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"final_errors = Dense(1, weights=[time_loss_weights, np.zeros(1)], trainable=False)(errors_by_time) # weight errors by time\n",
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"model = Model(inputs=inputs, outputs=final_errors)\n",
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"model.compile(loss='mean_absolute_error', optimizer='adam')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_1 (InputLayer) (None, 24, 20, 40, 7) 0 \n",
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"_________________________________________________________________\n",
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"pred_net_1 (PredNet) (None, 24, 3) 1621448 \n",
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"_________________________________________________________________\n",
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"time_distributed_1 (TimeDist (None, 24, 1) 4 \n",
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"_________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 24) 0 \n",
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"_________________________________________________________________\n",
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"dense_2 (Dense) (None, 1) 25 \n",
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"=================================================================\n",
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"Total params: 1,621,477\n",
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"Trainable params: 1,621,448\n",
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"Non-trainable params: 29\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_generator = SequenceGenerator(train_file, train_sources, nt, batch_size=batch_size, shuffle=False)\n",
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"val_generator = SequenceGenerator(val_file, val_sources, nt, batch_size=batch_size, N_seq=N_seq_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"lr_schedule = lambda epoch: 0.001 if epoch < 75 else 0.0001 # start with lr of 0.001 and then drop to 0.0001 after 75 epochs\n",
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"callbacks = [LearningRateScheduler(lr_schedule)]\n",
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"if save_model:\n",
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" if not os.path.exists(WEIGHTS_DIR): os.mkdir(WEIGHTS_DIR)\n",
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" callbacks.append(ModelCheckpoint(filepath=weights_file, monitor='val_loss', save_best_only=True))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/150\n",
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" - 21s - loss: 0.9718 - val_loss: 0.9142\n",
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"Epoch 2/150\n",
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" - 15s - loss: 0.9089 - val_loss: 0.8895\n",
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"Epoch 3/150\n",
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" - 15s - loss: 0.9057 - val_loss: 0.8826\n",
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"Epoch 4/150\n",
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" - 15s - loss: 0.8905 - val_loss: 0.8765\n",
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"Epoch 5/150\n",
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" - 15s - loss: 0.9106 - val_loss: 0.8840\n",
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"Epoch 6/150\n",
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" - 15s - loss: 0.8812 - val_loss: 0.8749\n",
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"Epoch 7/150\n",
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" - 15s - loss: 0.9099 - val_loss: 0.8897\n",
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"Epoch 8/150\n",
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" - 15s - loss: 0.8829 - val_loss: 0.8711\n",
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"Epoch 9/150\n",
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" - 15s - loss: 0.9039 - val_loss: 0.8843\n",
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"Epoch 10/150\n",
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" - 15s - loss: 0.8814 - val_loss: 0.8696\n",
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"Epoch 11/150\n",
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" - 15s - loss: 0.8897 - val_loss: 0.8881\n",
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"Epoch 12/150\n",
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" - 15s - loss: 0.8803 - val_loss: 0.8696\n",
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"Epoch 13/150\n",
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" - 15s - loss: 0.8819 - val_loss: 0.8836\n",
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"Epoch 14/150\n",
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" - 15s - loss: 0.8786 - val_loss: 0.8672\n",
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"Epoch 15/150\n",
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" - 15s - loss: 0.8732 - val_loss: 0.8671\n",
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"Epoch 16/150\n",
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" - 15s - loss: 0.8805 - val_loss: 0.8698\n",
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"Epoch 17/150\n",
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" - 15s - loss: 0.8643 - val_loss: 0.8600\n",
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"Epoch 18/150\n",
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" - 15s - loss: 0.8845 - val_loss: 0.8574\n",
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"Epoch 19/150\n",
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" - 16s - loss: 0.8581 - val_loss: 0.8568\n",
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"Epoch 20/150\n",
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" - 15s - loss: 0.8815 - val_loss: 0.8588\n",
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"Epoch 21/150\n",
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" - 15s - loss: 0.8622 - val_loss: 0.8575\n",
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"Epoch 22/150\n",
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" - 15s - loss: 0.8737 - val_loss: 0.8604\n",
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"Epoch 23/150\n",
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" - 15s - loss: 0.8619 - val_loss: 0.8568\n",
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"Epoch 24/150\n",
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" - 15s - loss: 0.8694 - val_loss: 0.8606\n",
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"Epoch 25/150\n",
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" - 15s - loss: 0.8638 - val_loss: 0.8542\n",
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"Epoch 26/150\n",
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" - 15s - loss: 0.8664 - val_loss: 0.8591\n",
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"Epoch 27/150\n",
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" - 15s - loss: 0.8700 - val_loss: 0.8551\n",
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"Epoch 28/150\n",
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" - 15s - loss: 0.8576 - val_loss: 0.8534\n",
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"Epoch 29/150\n",
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" - 15s - loss: 0.8751 - val_loss: 0.8512\n",
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"Epoch 30/150\n",
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" - 15s - loss: 0.8532 - val_loss: 0.8520\n",
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"Epoch 31/150\n",
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" - 15s - loss: 0.8758 - val_loss: 0.8518\n",
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"Epoch 32/150\n",
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" - 15s - loss: 0.8565 - val_loss: 0.8514\n",
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"Epoch 33/150\n",
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" - 15s - loss: 0.8688 - val_loss: 0.8500\n",
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"Epoch 34/150\n",
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" - 15s - loss: 0.8571 - val_loss: 0.8521\n",
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"Epoch 35/150\n",
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" - 15s - loss: 0.8659 - val_loss: 0.8501\n",
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"Epoch 36/150\n",
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" - 15s - loss: 0.8595 - val_loss: 0.8508\n",
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"Epoch 37/150\n",
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" - 15s - loss: 0.8634 - val_loss: 0.8512\n",
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"Epoch 38/150\n",
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" - 15s - loss: 0.8642 - val_loss: 0.8505\n",
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"Epoch 39/150\n",
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" - 15s - loss: 0.8562 - val_loss: 0.8490\n",
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"Epoch 40/150\n",
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" - 15s - loss: 0.8706 - val_loss: 0.8491\n",
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"Epoch 41/150\n",
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" - 15s - loss: 0.8498 - val_loss: 0.8490\n",
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"Epoch 42/150\n",
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" - 15s - loss: 0.8727 - val_loss: 0.8479\n",
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"Epoch 43/150\n",
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" - 15s - loss: 0.8519 - val_loss: 0.8473\n",
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"Epoch 44/150\n",
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" - 15s - loss: 0.8671 - val_loss: 0.8474\n",
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"Epoch 45/150\n",
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" - 15s - loss: 0.8534 - val_loss: 0.8485\n",
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"Epoch 46/150\n",
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" - 15s - loss: 0.8624 - val_loss: 0.8466\n",
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"Epoch 47/150\n",
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" - 15s - loss: 0.8563 - val_loss: 0.8467\n",
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"Epoch 48/150\n",
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" - 15s - loss: 0.8607 - val_loss: 0.8468\n",
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"Epoch 49/150\n",
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" - 15s - loss: 0.8594 - val_loss: 0.8475\n",
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"Epoch 50/150\n",
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" - 15s - loss: 0.8548 - val_loss: 0.8457\n",
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"Epoch 51/150\n",
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" - 15s - loss: 0.8671 - val_loss: 0.8456\n",
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"Epoch 52/150\n",
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" - 15s - loss: 0.8476 - val_loss: 0.8453\n",
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"Epoch 53/150\n",
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" - 15s - loss: 0.8701 - val_loss: 0.8452\n",
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"Epoch 54/150\n",
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" - 15s - loss: 0.8470 - val_loss: 0.8455\n",
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"Epoch 55/150\n",
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" - 15s - loss: 0.8674 - val_loss: 0.8447\n",
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"Epoch 56/150\n",
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" - 15s - loss: 0.8502 - val_loss: 0.8457\n",
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"Epoch 57/150\n",
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" - 15s - loss: 0.8614 - val_loss: 0.8443\n",
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"Epoch 58/150\n",
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" - 15s - loss: 0.8532 - val_loss: 0.8448\n",
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"Epoch 59/150\n",
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" - 15s - loss: 0.8590 - val_loss: 0.8443\n",
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"Epoch 60/150\n",
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" - 15s - loss: 0.8561 - val_loss: 0.8439\n",
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"Epoch 61/150\n",
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" - 15s - loss: 0.8543 - val_loss: 0.8441\n",
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"Epoch 62/150\n",
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" - 15s - loss: 0.8630 - val_loss: 0.8444\n",
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"Epoch 63/150\n",
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" - 15s - loss: 0.8472 - val_loss: 0.8437\n",
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"Epoch 64/150\n",
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" - 15s - loss: 0.8688 - val_loss: 0.8435\n",
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"Epoch 65/150\n",
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" - 15s - loss: 0.8435 - val_loss: 0.8439\n",
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"Epoch 66/150\n",
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" - 15s - loss: 0.8675 - val_loss: 0.8439\n",
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"Epoch 67/150\n",
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" - 15s - loss: 0.8484 - val_loss: 0.8432\n",
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"Epoch 68/150\n",
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" - 15s - loss: 0.8605 - val_loss: 0.8433\n",
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"Epoch 69/150\n",
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" - 15s - loss: 0.8508 - val_loss: 0.8438\n",
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"Epoch 70/150\n",
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" - 15s - loss: 0.8582 - val_loss: 0.8433\n",
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"Epoch 71/150\n",
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" - 15s - loss: 0.8531 - val_loss: 0.8426\n",
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"Epoch 72/150\n",
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" - 15s - loss: 0.8553 - val_loss: 0.8435\n",
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"Epoch 73/150\n",
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" - 15s - loss: 0.8601 - val_loss: 0.8425\n",
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"Epoch 74/150\n",
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" - 15s - loss: 0.8472 - val_loss: 0.8424\n",
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"Epoch 75/150\n",
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" - 15s - loss: 0.8659 - val_loss: 0.8425\n",
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"Epoch 76/150\n",
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" - 15s - loss: 0.8433 - val_loss: 0.8422\n",
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"Epoch 77/150\n",
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" - 15s - loss: 0.8670 - val_loss: 0.8423\n",
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"Epoch 78/150\n",
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" - 15s - loss: 0.8471 - val_loss: 0.8426\n",
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"Epoch 79/150\n",
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" - 15s - loss: 0.8607 - val_loss: 0.8426\n",
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"Epoch 80/150\n",
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" - 15s - loss: 0.8487 - val_loss: 0.8426\n",
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"Epoch 81/150\n",
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" - 15s - loss: 0.8582 - val_loss: 0.8426\n",
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"Epoch 82/150\n",
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" - 15s - loss: 0.8521 - val_loss: 0.8424\n",
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"Epoch 83/150\n",
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" - 15s - loss: 0.8558 - val_loss: 0.8424\n",
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"Epoch 84/150\n",
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" - 15s - loss: 0.8576 - val_loss: 0.8421\n",
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"Epoch 85/150\n",
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" - 15s - loss: 0.8489 - val_loss: 0.8421\n",
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"Epoch 86/150\n",
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" - 15s - loss: 0.8648 - val_loss: 0.8421\n",
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"Epoch 87/150\n",
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" - 15s - loss: 0.8431 - val_loss: 0.8421\n",
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"Epoch 88/150\n",
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" - 15s - loss: 0.8673 - val_loss: 0.8422\n",
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"Epoch 89/150\n",
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" - 15s - loss: 0.8460 - val_loss: 0.8424\n",
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"Epoch 90/150\n",
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" - 15s - loss: 0.8621 - val_loss: 0.8425\n",
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"Epoch 91/150\n",
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" - 15s - loss: 0.8481 - val_loss: 0.8425\n",
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"Epoch 92/150\n",
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" - 15s - loss: 0.8578 - val_loss: 0.8426\n",
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"Epoch 93/150\n",
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" - 15s - loss: 0.8518 - val_loss: 0.8424\n",
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"Epoch 94/150\n",
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" - 15s - loss: 0.8564 - val_loss: 0.8424\n",
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"Epoch 95/150\n",
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" - 15s - loss: 0.8556 - val_loss: 0.8421\n",
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"Epoch 96/150\n",
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" - 15s - loss: 0.8506 - val_loss: 0.8421\n",
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"Epoch 97/150\n",
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" - 15s - loss: 0.8637 - val_loss: 0.8419\n",
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"Epoch 98/150\n",
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" - 15s - loss: 0.8438 - val_loss: 0.8420\n",
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"Epoch 99/150\n",
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" - 15s - loss: 0.8672 - val_loss: 0.8421\n",
|
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"Epoch 100/150\n",
|
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" - 15s - loss: 0.8437 - val_loss: 0.8422\n",
|
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"Epoch 101/150\n",
|
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" - 15s - loss: 0.8645 - val_loss: 0.8423\n",
|
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"Epoch 102/150\n",
|
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" - 15s - loss: 0.8472 - val_loss: 0.8424\n",
|
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"Epoch 103/150\n",
|
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" - 15s - loss: 0.8589 - val_loss: 0.8425\n",
|
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"Epoch 104/150\n",
|
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" - 15s - loss: 0.8508 - val_loss: 0.8424\n",
|
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"Epoch 105/150\n",
|
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" - 15s - loss: 0.8567 - val_loss: 0.8425\n",
|
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"Epoch 106/150\n",
|
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" - 15s - loss: 0.8542 - val_loss: 0.8421\n",
|
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"Epoch 107/150\n",
|
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" - 15s - loss: 0.8522 - val_loss: 0.8421\n",
|
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"Epoch 108/150\n",
|
|
" - 15s - loss: 0.8613 - val_loss: 0.8419\n",
|
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"Epoch 109/150\n",
|
|
" - 15s - loss: 0.8454 - val_loss: 0.8419\n",
|
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"Epoch 110/150\n",
|
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" - 15s - loss: 0.8672 - val_loss: 0.8420\n",
|
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"Epoch 111/150\n",
|
|
" - 15s - loss: 0.8420 - val_loss: 0.8420\n",
|
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"Epoch 112/150\n",
|
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" - 15s - loss: 0.8660 - val_loss: 0.8422\n",
|
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"Epoch 113/150\n",
|
|
" - 15s - loss: 0.8470 - val_loss: 0.8423\n",
|
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"Epoch 114/150\n",
|
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" - 15s - loss: 0.8593 - val_loss: 0.8424\n",
|
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"Epoch 115/150\n",
|
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" - 15s - loss: 0.8498 - val_loss: 0.8423\n",
|
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"Epoch 116/150\n",
|
|
" - 15s - loss: 0.8572 - val_loss: 0.8425\n",
|
|
"Epoch 117/150\n",
|
|
" - 15s - loss: 0.8522 - val_loss: 0.8421\n",
|
|
"Epoch 118/150\n",
|
|
" - 15s - loss: 0.8545 - val_loss: 0.8421\n",
|
|
"Epoch 119/150\n",
|
|
" - 15s - loss: 0.8593 - val_loss: 0.8418\n",
|
|
"Epoch 120/150\n",
|
|
" - 15s - loss: 0.8466 - val_loss: 0.8418\n",
|
|
"Epoch 121/150\n",
|
|
" - 15s - loss: 0.8653 - val_loss: 0.8418\n",
|
|
"Epoch 122/150\n",
|
|
" - 15s - loss: 0.8429 - val_loss: 0.8418\n",
|
|
"Epoch 123/150\n",
|
|
" - 15s - loss: 0.8667 - val_loss: 0.8420\n",
|
|
"Epoch 124/150\n",
|
|
" - 15s - loss: 0.8467 - val_loss: 0.8422\n",
|
|
"Epoch 125/150\n",
|
|
" - 15s - loss: 0.8603 - val_loss: 0.8422\n",
|
|
"Epoch 126/150\n",
|
|
" - 15s - loss: 0.8483 - val_loss: 0.8422\n",
|
|
"Epoch 127/150\n",
|
|
" - 15s - loss: 0.8578 - val_loss: 0.8424\n",
|
|
"Epoch 128/150\n",
|
|
" - 15s - loss: 0.8517 - val_loss: 0.8421\n",
|
|
"Epoch 129/150\n",
|
|
" - 15s - loss: 0.8554 - val_loss: 0.8422\n",
|
|
"Epoch 130/150\n",
|
|
" - 15s - loss: 0.8572 - val_loss: 0.8418\n",
|
|
"Epoch 131/150\n",
|
|
" - 15s - loss: 0.8484 - val_loss: 0.8418\n",
|
|
"Epoch 132/150\n",
|
|
" - 15s - loss: 0.8645 - val_loss: 0.8417\n",
|
|
"Epoch 133/150\n",
|
|
" - 15s - loss: 0.8427 - val_loss: 0.8417\n",
|
|
"Epoch 134/150\n",
|
|
" - 15s - loss: 0.8669 - val_loss: 0.8419\n",
|
|
"Epoch 135/150\n",
|
|
" - 15s - loss: 0.8456 - val_loss: 0.8420\n",
|
|
"Epoch 136/150\n",
|
|
" - 15s - loss: 0.8617 - val_loss: 0.8421\n",
|
|
"Epoch 137/150\n",
|
|
" - 15s - loss: 0.8477 - val_loss: 0.8422\n",
|
|
"Epoch 138/150\n",
|
|
" - 15s - loss: 0.8574 - val_loss: 0.8423\n",
|
|
"Epoch 139/150\n",
|
|
" - 15s - loss: 0.8515 - val_loss: 0.8421\n",
|
|
"Epoch 140/150\n",
|
|
" - 15s - loss: 0.8560 - val_loss: 0.8422\n",
|
|
"Epoch 141/150\n",
|
|
" - 15s - loss: 0.8553 - val_loss: 0.8419\n",
|
|
"Epoch 142/150\n",
|
|
" - 15s - loss: 0.8502 - val_loss: 0.8418\n",
|
|
"Epoch 143/150\n",
|
|
" - 15s - loss: 0.8633 - val_loss: 0.8416\n",
|
|
"Epoch 144/150\n",
|
|
" - 15s - loss: 0.8434 - val_loss: 0.8416\n",
|
|
"Epoch 145/150\n",
|
|
" - 15s - loss: 0.8668 - val_loss: 0.8417\n",
|
|
"Epoch 146/150\n",
|
|
" - 15s - loss: 0.8433 - val_loss: 0.8418\n",
|
|
"Epoch 147/150\n",
|
|
" - 15s - loss: 0.8642 - val_loss: 0.8419\n",
|
|
"Epoch 148/150\n",
|
|
" - 15s - loss: 0.8468 - val_loss: 0.8421\n",
|
|
"Epoch 149/150\n",
|
|
" - 15s - loss: 0.8585 - val_loss: 0.8421\n",
|
|
"Epoch 150/150\n",
|
|
" - 15s - loss: 0.8504 - val_loss: 0.8420\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"history = model.fit_generator(train_generator, steps_per_epoch=(samples_per_epoch / batch_size), \n",
|
|
" epochs=nb_epoch, callbacks=callbacks,\n",
|
|
" validation_data=val_generator, validation_steps=N_seq_val / batch_size,\n",
|
|
" verbose=2, workers=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"if save_model:\n",
|
|
" json_string = model.to_json()\n",
|
|
" with open(json_file, \"w\") as f:\n",
|
|
" f.write(json_string)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": "3.6.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|