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.
241 lines
6.9 KiB
Plaintext
241 lines
6.9 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 os\n",
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"import numpy as np\n",
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"from six.moves import cPickle\n",
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"import matplotlib\n",
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"matplotlib.use('Agg')\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.gridspec as gridspec\n",
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"%matplotlib inline\n",
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"from keras import backend as K\n",
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"from keras.models import Model, model_from_json\n",
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"from keras.layers import Input, Dense, Flatten\n",
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"\n",
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"from prednet import PredNet\n",
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"from data_utils import SequenceGenerator\n",
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"\n",
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"from tqdm import tqdm"
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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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"n_plot = 40\n",
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"batch_size = 10\n",
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"nt = 24"
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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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"WEIGHTS_DIR = '../Training/weights/'\n",
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"DATA_DIR = '../data/'\n",
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"RESULTS_SAVE_DIR = './weather_results/'"
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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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"weights_file = os.path.join(WEIGHTS_DIR, 'prednet_weather_weights.hdf5')\n",
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"json_file = os.path.join(WEIGHTS_DIR, 'prednet_weather_model.json')\n",
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"test_file = os.path.join(DATA_DIR, 'x_test.hkl')\n",
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"test_sources = os.path.join(DATA_DIR, 'sources_test.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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"# Load trained model\n",
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"f = open(json_file, 'r')\n",
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"json_string = f.read()\n",
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"f.close()\n",
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"train_model = model_from_json(json_string, custom_objects = {'PredNet': PredNet})\n",
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"train_model.load_weights(weights_file)"
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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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"# Create testing model (to output predictions)\n",
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"layer_config = train_model.layers[1].get_config()\n",
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"layer_config['output_mode'] = 'prediction'\n",
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"data_format = layer_config['data_format'] if 'data_format' in layer_config else layer_config['dim_ordering']\n",
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"test_prednet = PredNet(weights=train_model.layers[1].get_weights(), **layer_config)\n",
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"input_shape = list(train_model.layers[0].batch_input_shape[1:])\n",
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"input_shape[0] = nt\n",
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"inputs = Input(shape=tuple(input_shape))\n",
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"predictions = test_prednet(inputs)\n",
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"test_model = Model(inputs=inputs, outputs=predictions)"
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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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"test_generator = SequenceGenerator(test_file, test_sources, nt, sequence_start_mode='unique', data_format=data_format)\n",
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"X_test = test_generator.create_all()\n",
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"X_hat = test_model.predict(X_test, batch_size)\n",
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"if data_format == 'channels_first':\n",
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" X_test = np.transpose(X_test, (0, 1, 3, 4, 2))\n",
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" X_hat = np.transpose(X_hat, (0, 1, 3, 4, 2))"
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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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"# Compare MSE of PredNet predictions vs. using last frame. Write results to prediction_scores.txt\n",
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"mse_model = np.nanmean( (X_test[:, 1:] - X_hat[:, 1:])**2 ) # look at all timesteps except the first\n",
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"mse_prev = np.nanmean( (X_test[:, :-1] - X_test[:, 1:])**2 )\n",
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"if not os.path.exists(RESULTS_SAVE_DIR): os.mkdir(RESULTS_SAVE_DIR)\n",
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"f = open(RESULTS_SAVE_DIR + 'prediction_scores.txt', 'w')\n",
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"f.write(\"Model MSE: %f\\n\" % mse_model)\n",
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"f.write(\"Previous Frame MSE: %f\" % mse_prev)\n",
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"f.close()"
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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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"Model MSE:\t 14.119876861572266\n",
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"Prev Frame MSE:\t 0.02834348939359188\n"
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]
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}
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],
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"source": [
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"print(\"Model MSE:\\t {}\\nPrev Frame MSE:\\t {}\".format(mse_model,mse_prev))"
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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": null,
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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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" 63%|███████████████████████████████████████████████████▉ | 19/30 [09:38<05:35, 30.47s/it]"
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]
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}
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],
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"source": [
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"# Plot some predictions\n",
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"aspect_ratio = float(X_hat.shape[3]) / X_hat.shape[2]\n",
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"plt.figure(figsize = (nt, 7*2*aspect_ratio))\n",
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"gs = gridspec.GridSpec(2*7, nt)\n",
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"gs.update(wspace=0., hspace=0.2)\n",
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"plot_save_dir = os.path.join(RESULTS_SAVE_DIR, 'prediction_plots/')\n",
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"if not os.path.exists(plot_save_dir): os.mkdir(plot_save_dir)\n",
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"plot_idx = np.random.permutation(X_test.shape[0])[:n_plot]\n",
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"for i in tqdm(plot_idx):\n",
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" for t in range(nt):\n",
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" for c in range(7):\n",
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" plt.subplot(gs[t + c*2*nt])\n",
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" plt.imshow(X_test[i,t,:,:,c], interpolation='none')\n",
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" plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')\n",
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" if t==0: plt.ylabel('Actual', fontsize=10)\n",
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"\n",
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" plt.subplot(gs[t + (c*2+1)*nt])\n",
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" plt.imshow(X_hat[i,t,:,:,c], interpolation='none')\n",
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" plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')\n",
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" if t==0: plt.ylabel('Predicted', fontsize=10)\n",
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"\n",
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" plt.savefig(plot_save_dir + 'plot_' + str(i) + '.png')\n",
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" plt.clf()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig=plt.figure(figsize=(15,10))\n",
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"columns = 3\n",
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"rows = 4\n",
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"for i in range(1,columns+rows +1):\n",
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" fig.add_subplot(rows,columns,i)\n",
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" plt.imshow(X_test[0,0,:,:,i-1],X_hat[0,0,:,:,i-1])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_hat[0][0][0][0][2]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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