peilaus alkaen https://github.com/prise6/smart-iss-posts
update draft notebook
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vanhempi
11cdb40b3f
commit
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#%% [markdown]
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# # Clustering classique
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#%% [markdown]
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# ## import classique
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import os
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#%%
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%load_ext autoreload
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%autoreload 2
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os.chdir('/home/jovyan/work')
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#%% [markdown]
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# ## Import iss
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#%%
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from iss.tools import Config
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from iss.tools import Tools
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from iss.models import SimpleConvAutoEncoder
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from iss.clustering import ClassicalClustering
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from iss.clustering import AdvancedClustering
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from dotenv import find_dotenv, load_dotenv
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import numpy as np
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#%% [markdown]
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# ## Chargement de la config
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#%%
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load_dotenv(find_dotenv())
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cfg = Config(project_dir = os.getenv("PROJECT_DIR"), mode = os.getenv("MODE"))
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#%% [markdown]
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# ## Chargement du modèle
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#%%
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## charger le modèle
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model_type = 'simple_conv'
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cfg.get('models')[model_type]['model_name'] = 'model_colab'
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model = SimpleConvAutoEncoder(cfg.get('models')[model_type])
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#%% [markdown]
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## Chargement des images
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#%%
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filenames = Tools.list_directory_filenames('data/processed/models/autoencoder/train/k/')
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (27, 48), batch = 496, nb_batch = 10, scale = 1/255)
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#%%
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pictures_id, pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model)
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#%%
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intermediate_output = pictures_preds.reshape((pictures_preds.shape[0], 3*6*16))
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#%%
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clustering = AdvancedClustering(cfg.get('clustering')['advanced'], pictures_id, intermediate_output)
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#%%
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clustering.compute_pca()
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#%%
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clustering.compute_kmeans()
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#%%
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clustering.compute_kmeans_centers()
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#%%
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len(clustering.kmeans_centers)
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#%%
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clustering.dbscan_args = {'eps': 50, 'min_samples':1}
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clustering.compute_dbscan()
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#%%
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clustering.compute_dbscan_labels()
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#%%
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len(clustering.final_labels)
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#%%
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np.unique(clustering.final_labels, return_counts = True)
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#%%[markdown]
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# # Graphiques
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#%%
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def select_cluster(clustering, id_cluster):
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return [os.path.join('data/processed/models/autoencoder/train/k/', res[0] + '.jpg') for res in clustering.get_zip_results() if res[2] == id_cluster]
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#%%
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for cl in np.unique(clustering.kmeans_labels):
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print("Cluster %s" % (cl))
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res_tmp = select_cluster(clustering, cl)
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if len(res_tmp) >= 0:
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print(len(res_tmp))
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image_array = [Tools.read_np_picture(f, target_size = (54, 96)) for f in res_tmp[:100]]
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img = Tools.display_mosaic(image_array, nrow = 10)
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fig = plt.figure(1, figsize=(12, 7))
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plt.imshow(img, aspect = 'auto')
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plt.show()
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#%% [markdown]
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# ## faut essayer de faire des paquets
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#%%
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from sklearn.manifold import TSNE
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output_tnse = TSNE(n_components=2).fit_transform(clustering.pca_reduction)
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#%%
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plt.scatter(
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output_tnse[:,0],
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output_tnse[:,1],
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c = clustering.kmeans_labels
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)
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plt.show()
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#%%
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from sklearn.cluster import KMeans
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tmp_km = KMeans(n_clusters = 15)
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tmp_res = tmp_km.fit(output_tnse)
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#%%
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tmp_res.labels_
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#%%
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plt.scatter(
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output_tnse[:,0],
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output_tnse[:,1],
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c = tmp_res.labels_
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)
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plt.show()
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#%%
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clustering.final_labels = tmp_res.labels_
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#%%
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from scipy.cluster.hierarchy import dendrogram
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from sklearn.cluster import AgglomerativeClustering
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#%%
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def plot_dendrogram(model, **kwargs):
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# Children of hierarchical clustering
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children = model.children_
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# Distances between each pair of children
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# Since we don't have this information, we can use a uniform one for plotting
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distance = np.arange(children.shape[0])
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# The number of observations contained in each cluster level
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no_of_observations = np.arange(2, children.shape[0]+2)
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# Create linkage matrix and then plot the dendrogram
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linkage_matrix = np.column_stack([children, distance, no_of_observations]).astype(float)
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# Plot the corresponding dendrogram
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dendrogram(linkage_matrix, **kwargs)
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#%%
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cah_fit = AgglomerativeClustering(n_clusters=10)
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#%%
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cah_fit = cah_fit.fit(clustering.kmeans_centers)
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#%%
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fig = plt.figure(1, figsize=(12, 7))
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plot_dendrogram(cah_fit, labels = cah_fit.labels_)
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#%%
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cah_fit.labels_
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#%%
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tmp = Tools.read_np_picture('data/processed/models/autoencoder/train/k/20171109-192001.jpg',target_size = (27, 48), scale = 1/255)
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tmp = tmp.reshape((1,27,48,3))
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np.sum(model.get_encoded_prediction(tmp))
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#%%
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filenames = Tools.list_directory_filenames('data/processed/models/autoencoder/train/k/')
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generator_imgs = Tools.generator_np_picture_from_filenames(filenames, target_size = (27, 48), batch = 10, nb_batch = 3, scale = 1/255)
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predictions_list = []
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predictions_id = []
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for imgs in generator_imgs:
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predictions_id.append(imgs[0])
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predictions_list.append(model.get_encoded_prediction(imgs[1]))
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#%%
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np.concatenate(tuple(predictions_list), axis = 0)[0,:,:,:]
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#%%
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predictions_list[0][0,:,:,:]
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#%%
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print(pictures_preds[1,:,:,:])
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#%%
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pictures_preds.shape
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#%%
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@ -18,6 +18,7 @@ from iss.tools import Config
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from iss.tools import Tools
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from iss.models import SimpleConvAutoEncoder
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from iss.clustering import ClassicalClustering
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from iss.clustering import AdvancedClustering
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from dotenv import find_dotenv, load_dotenv
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import numpy as np
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@ -149,7 +150,3 @@ plt.scatter(clustering.pca_reduction[:, 0], clustering.pca_reduction[:, 1], c =
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#%%
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plt.scatter(clustering.pca_reduction[np.array(col) == 1, 0], clustering.pca_reduction[np.array(col) == 1, 1])
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#%%
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@ -147,47 +147,28 @@
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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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"i_debut:0\n",
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"i_fin:496\n",
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"i_debut:496\n",
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"i_fin:992\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"pictures_preds = Tools.encoded_pictures_from_generator(generator_imgs, model)"
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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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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(992, 3, 6, 16)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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"ename": "AttributeError",
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"evalue": "'tuple' object has no attribute 'reshape'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-13-e3d22d0becf7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintermediate_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpictures_preds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m992\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'reshape'"
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]
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}
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],
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"source": [
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"pictures_preds.shape"
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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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"intermediate_output = pictures_preds.reshape((992, 3*6*16))"
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]
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File diff suppressed because one or more lines are too long
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@ -31,17 +31,21 @@
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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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"execution_count": 4,
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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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"Requirement already satisfied: MySQL-connector-python in /opt/conda/lib/python3.6/site-packages (8.0.15)\n",
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"Requirement already satisfied: protobuf>=3.0.0 in /opt/conda/lib/python3.6/site-packages (from MySQL-connector-python) (3.6.1)\n",
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"Collecting MySQL-connector-python\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f7/59/c2220c52d747da492f2aed108cdf99b640b88cf89dbbe2ea13a8c04201aa/mysql_connector_python-8.0.18-cp36-cp36m-manylinux1_x86_64.whl (16.1MB)\n",
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"\u001b[K 100% |████████████████████████████████| 16.1MB 4.1MB/s \n",
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"\u001b[?25hRequirement already satisfied: protobuf>=3.0.0 in /opt/conda/lib/python3.6/site-packages (from MySQL-connector-python) (3.6.1)\n",
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"Requirement already satisfied: six>=1.9 in /opt/conda/lib/python3.6/site-packages (from protobuf>=3.0.0->MySQL-connector-python) (1.12.0)\n",
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"Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from protobuf>=3.0.0->MySQL-connector-python) (40.8.0)\n"
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"Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from protobuf>=3.0.0->MySQL-connector-python) (40.8.0)\n",
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"Installing collected packages: MySQL-connector-python\n",
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"Successfully installed MySQL-connector-python-8.0.18\n"
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]
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}
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],
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@ -51,7 +55,7 @@
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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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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -66,9 +70,21 @@
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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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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "__init__() missing 2 required positional arguments: 'project_dir' and 'mode'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-6-e6f50bbb757a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'project_dir' and 'mode'"
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]
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}
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],
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"source": [
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"cfg = Config()"
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]
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