Tag: ‘dice’]

Bitsler Crypto Gambling Promo Code

Post author By bitcoin
Post date September 23, 2020
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Bitsler offers casino games like dice and roulette with bitcoin, ethereum, ripple and 17 other cryptocurrencies. Promo Code: limit or use this link. “Join Bitsler Today ☆ Play with 24/7 LIVE Dealers, Slots, Dice and Table Games ☆ Register” Bitsler Referral Code BITSLER.COM Promo Code: click here

Tags 'dice'], 'slots': {}, crash, Roulette

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WTFSkins Promo Code | CSGO Gambling Site

Post author By bitcoin
Post date September 7, 2020
No Comments on WTFSkins Promo Code | CSGO Gambling Site

Promo Code: limityt or use this link. WTFSkins.com is the top CSGO Gambling website 2019! Deposit and withdraw your favorite CS:GO Skins today and play roulette, crash, and dice now! Also accept VGO skins! “Deposit and withdraw your favorite CS:GO Skins at WTFSkins today and play roulette, crash, and dice now!” WTFSkins Referral Code WTFSkins.com […]

Tags 'dice'], 'upgrade', crash, Jackpot, Roulette

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CSGOPolygon Gambling Site Promo Code

Post author By bitcoin
Post date August 23, 2020
No Comments on CSGOPolygon Gambling Site Promo Code

Promo Code: limityt or use this link. CSGOPolygon.com site that offers several different games to wager on as well as CSGO betting. “CSGOPolygon offers Roulette, Dice, Match Betting and Crash” CSGO Polygon Referral Code CSGOPolygon.com Promo Code: limityt

Tags 'dice'], crash, Esports Betting, Roulette, tournaments

Mastering Development

r glmmLasso : Error in n %*% s (glmm.rmd#64): requires numeric/complex matrix/vector arguments

Post author By Full Stack
Post date July 7, 2020
No Comments on r glmmLasso : Error in n %*% s (glmm.rmd#64): requires numeric/complex matrix/vector arguments

I am trying to fit a glmmLasso model using this code: lasso <- glmmLasso(stars ~ ADJRIND + AUC + KAPPA + DICE + ICCORR + JACRD + MUTINF + RNDIND + SURFOVLP + SURFDICE + VOLSMTY + HDRFDST + AVGDIST + MAHLNBS + VARINFO + GCOERR + PROBDST + SNSVTY + SPCFTY + PRCISON + […]

Tags 'dice'], 'simple', "545", "ablaze_gull", "ACURCY", "ADJRIND", "AVGDIST", "BIIPL-rnd", "brats_2013_pat0116_1", "brats_2013_pat0130_1", "brats_2013_pat0134_1", "brats_2019_138_1", "brats_2019_141_1", "brats_2019_90_1", "brats_MDA_945_1", "FightGliomas-rnd", "FMEASR", "GCOERR", "GTVOL", "HDRFDST", "ICCORR", "JACRD", "jaguars-rnd", "MAHLNBS", "Misfits-rnd", "MUTINF", "NJIT4321-rnd", "PRCISON", "PROBDST", "RNDIND", "SEGVOL" "1", "SNSVTY", "SPCFTY", "SURFDICE", "SURFOVLP", "Tyagi-rnd", "UTintelligence-rnd", "VARINFO", "VOLSMTY", "zyx", ("TN", (algorithm, \kappa}\, &rnd, $condition = "", 0, 0.000484468008443052, 0.000985021226653005, 0.00156799523467276, 0.00221329648095196, 0.00224566249801225, 0.00311219341105724, 0.00334963095263752, 0.00376678772882303, 0.00392992101733526, 0.00412306131465471, 0.00441476139981669, 0.00445229135652993, 0.00460614018952978, 0.00464629073617492, 0.00592110152408344, 0.00656238844620888, 0.00663399526436313, 0.00666139521334549, 0.0068434072272868, 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0.972853877376545, 0.974069012629503, 0.974186271807427, 0.974220482589075, 0.975025528879729, 0.975692379970523, 0.975754720290816, 0.976878772829282, 0.976890577030139, 0.977103022412987, 0.977337562590699, 0.977814120503358, 0.978056597734505, 0.979900170507277, 0.980082675921329, 0.981797036327553, 0.981882531870561, 0.982142000911457, 0.982446168742101, 0.983443927479636, 0.983989082060977, 0.984029969073529, 0.984046725668933, 0.984621691861738, 0.984701258781098, 0.985270803169769, 0.985320290447796, 0.986032615479199, 0.986913573761583, 0.986931119159787, 0.987093362528072, 0.98790705513673, 0.988377655513698, 0.98876460334073, 0.990145970199641, 0.990706184810191, 0.990789770039584, 0.991170938410922, 0.991917129836001, 0.991938377123562, 0.991951857957291, 0.992079844226351, 0.992240792064425, 0.992684550205198, 0.992962414786454, 0.99305423685419, 0.993156592772713, 0.993254294603284, 0.993338604786654, 0.99340680392773, 0.993437611553791, 0.993911405460269, 0.993957447335076, 0.994766944943347, 0.994785970651161, 0.9948496225819, 0.995299728580093, 0.99554770864347, 0.995585238600183, 0.995843213411434, 0.995876938685345, 0.996070078982665, 0.996233212271177, 0.996610565192861, 0.996650369047362, 0.996885441695005, 0.996887806588943, 0.996965340044328, 0.997373332439284, 0.997383637222852, 0.997641026050083, 0.99773209120061, 0.997754337501988, 0.997786703519048, 0.998264235195115, 0.998432004765327, 0.99851769262895, 0.999014978773347, 0.999515531991557, 1, 1.33323405520428, 10.1585752147551, 1003.22222222222, 1019.44444444444 "14", 1066.88888888889, 107.222222222222, 1122.22222222222, 12.3514245536757, 12.6546214655814, 120.333333333333, 1208.11111111111 "9", 1218.22222222222 "8", 1266.77777777778 "10", 131.444444444444, 131.888888888889, 1311, 136.555555555556, 136.666666666667, 14.3628067945127, 14.6242696243608, 141.222222222222, 144.555555555556, 16.5906961635536, 175.666666666667, 18.5555555555556, 180.111111111111, 18413.5555555556, 18489.7777777778, 18493.4444444444, 188.777777777778, 19.2537294363992, 19.3333333333333, 19.7777777777778, 19.8501973757616, 19740.8888888889, 198.222222222222, 2.35337109861099, 2.41851089469065, 2.61846276424863, 20050.4444444444, 20051.1111111111, 20064.1111111111, 20078.1111111111, 20081.4444444444, 20432.4444444444, 20438.1111111111, 20442.4444444444, 20582.4444444444, 20606, 20630.6666666667, 20723.3333333333, 20733.3333333333, 20804.6666666667, 20910.4444444444, 20924, 21.7259079503732, 244.111111111111, 26.6666666666667, 3, 3.15931022051951, 3.33333333333333, 3.4166433002747, 3.56344098531587, 3.66666666666667, 30.7777777777778, 32.7153630434273, 34.2222222222222, 4.24374989738083, 4.33333333333333, 4.66666666666667, 4.81983416902026, 43.7777777777778, 44.4444444444444, 44.5555555555556, 447.111111111111, 481.777777777778, 5, 5.33333333333333, 5.66666666666667, 5.87456263780313, 5.88313878476988, 500.444444444444, 509.777777777778 "13", 51.4444444444444, 510.777777777778, 530.111111111111 "12", 54.7777777777778, 56.6666666666667, 574.555555555556, 583.777777777778 "11", 6, 6.97899055058698, 60.3333333333333, 669.333333333333, 7.45373777515636, 7.68751024370488, 717.111111111111, 719.888888888889, 722.333333333333, 725.222222222222, 736.444444444444, 746, 749.333333333333, 762.111111111111, 769, 770.222222222222, 78.8888888888889, 785.555555555556, 788.777777777778, 79.2222222222222, 8.49706824661522, 80.1111111111111, 800.777777777778, 801 "17", 801.555555555556 "7", 803.444444444444 "6", 806.333333333333, 806.555555555556 "18", 81.1111111111111, 812.777777777778 "19", 814.444444444444 "5", 824.888888888889 "4", 83.5555555555556, 84), 841.777777777778 "3", 86.4444444444444, 867.555555555556 "2", 882.888888888889, 89.2222222222222, 892.777777777778, 897.222222222222 "20", 9.02494724371663, 9.33333333333333, 906, 917.444444444444 edit I somehow got a calculation running now by reducing some predictors and switching the family variable and getting rid, 92.4444444444444, 923.111111111111, 966.333333333333 "16", 97.2222222222222, 97.8888888888889, 979.777777777778 "15", auc, control=list(print.iter=TRUE)) However the calculation is stuck at iteration 89 for 30minutes now. I have trouble deciding which one is the, control=list(print.iter=TRUE)) I get the following error msg: Error in n %*% s (glmm.rmd#64): requires numeric/complex matrix/vector argume, data = mixedModel_df, Fallout, family = acat(), family = cumulative(), fn, fp, I am trying to fit a glmmLasso model using this code: lasso <- glmmLasso(stars ~ ADJRIND + AUC + KAPPA + DICE + ICCORR + JACRD + MUTINF + R, lambda=10, my final model will have two?, only my random variable is a factor. Also what should I specify for family? As I requested here the head of my dataframe: "", participant, patient: {}, recall, rnd = ~1|participant, rnd = NULL, stars, switch.NR=TRUE, tp

CMS Mastering Development

Import / Aggregation DB – with 50 Million Records – Best Practice?

Post author By Jessica Alba
Post date April 29, 2020
No Comments on Import / Aggregation DB – with 50 Million Records – Best Practice?

I’m working on my second Craft project ever. (First was an ultra-simple site just to learn the basics.) With this new project I need to accomplish 2 things… (1) – Private – Import/aggregate mass data from external sources based on my specific search parameters (think YouTube, CNN, BBC, Instagram, Reddit, Imgur, Pinterest, etc). Then slice, […]

Tags 'dice'], "BBC", and editorialize that data to create content-rich, building newly curated feeds) - and another to grab and serve the Public (post-curation) results?, categorize, CNN, compiling, curate, editing, etc). Then slice, headLine, I'm working on my second Craft project ever. (First was an ultra-simple site just to learn the basics.) With this new project I need to acco, Imgur, instagram, pinterest, Reddit, topical feeds. (2) - Public - Present those content-rich feeds as dense Topical Resources for my website visitors. Craft is a brilliant sol

Mastering Development

I followed the tensorflow image segmentation tutorial, but the predicted mask is blank

Post author By Full Stack
Post date March 15, 2020
No Comments on I followed the tensorflow image segmentation tutorial, but the predicted mask is blank

I’d like to try image segmentation with my grayscale tif images (the shape of original images are (512,512) and the value of each pixel is between 0-2 or nan which is in float32 type and the mask images have 0, 1, or nan also in float32 type). I followed Google Colab and tensorflow tutorialto create […]

Tags :, 'dice'], 'Predicted Mask'] for i in range(len(display_list)): plt.subplot(1, 'True Mask', [-1]) intersection = tf.reduce_sum(y_true_f * y_pred_f) score = (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_s, [-1]) y_pred_f = tf.reshape(y_pred, [j])) for k, [l])) #for visualization def display(display_list): plt.figure(figsize=(15, 0, 0] display([sample_image, 0] sample_mask = mask.numpy()[:, 0] show_predictions(test_dataset, 1, 1) batch_size = 8 buffer_size = 5 epochs = 5 train_dataset = train.cache().shuffle(train_length).batch(batch_size).repeat() train_dataset =, 1) plt.plot(epochs_range, 1024) # center decoder4 = decoder_block(center, 128) # 32 encoder3_pool, 128) # 64 decoder1 = decoder_block(decoder2, 15)) title = ['Input Image', 2, 2) plt.plot(epochs_range, 2))(encoder) return encoder_pool, 256) # 16 encoder4_pool, 256) # 32 decoder2 = decoder_block(decoder3, 3, 3) The output of the prediction is below: The output of predictions I tried to inspect the variables test and test_dataset using: for img, 32) # 128 encoder1_pool, 32) # 256 outputs = layers.Conv2D(1, 512) # 16 decoder3 = decoder_block(decoder4, 512) # 8 center = conv_block(encoder4_pool, 512) and the value of each pixel is between 0-2 or nan which is in float32 type and the mask images have 0, 512)), 64) # 128 decoder0 = decoder_block(decoder1, 64) # 64 encoder2_pool, 8)) plt.subplot(1, activation='sigmoid')(decoder0) model = models.Model(inputs=[inputs], axis=-1) decoder = layers.BatchNormalization()(decoder) decoder = layers.Activation('relu')(decoder) decoder = layers.Conv2D(num_, axis=-1) pred_mask = pred_mask[..., axis=-1) test_img.append(stacked_img) test_mask = [] for p in mask_val: mask= Image.open(p) mask_arr = np.array(mask) stacke, axis=-1) test_mask.append(stacked_mask) #create TensorSliceDataset for i, axis=-1) train_image.append(stacked_img) train_mask = [] for n in mask: mask= Image.open(n) mask_arr= np.array(mask) stacked, axis=-1) train_mask.append(stacked_mask) test_img = [] for o in img_val: img= Image.open(o) img_arr = np.array(img) stacked_, callbacks=[cp]) #training process visualization dice = history.history['dice_loss'] val_dice = history.history['val_dice_loss'] loss = hist, concat_tensor, create_mask(pred_mask)]) def create_mask(pred_mask): pred_mask = tf.argmax(pred_mask, decoder], encoder def decoder_block(input_tensor, encoder0, encoder0 = encoder_block(inputs, encoder1, encoder1 = encoder_block(encoder0_pool, encoder2, encoder2 = encoder_block(encoder1_pool, encoder3, encoder3 = encoder_block(encoder2_pool, encoder4, encoder4 = encoder_block(encoder3_pool, epochs=epochs, how do I set the value for nan if I hope the model can ignore sea? Thank you for your help., I'd like to try image segmentation with my grayscale tif images (the shape of original images are (512, i+1) plt.title(title[i]) plt.imshow(display_list[i]) plt.axis('off') plt.show() for img, img_val, j in zip(train_image, l in zip(test_img, label='Training Dice Loss') plt.plot(epochs_range, label='Training Loss') plt.plot(epochs_range, label='Validation Dice Loss') plt.legend(loc='upper right') plt.title('Training and Validation Dice Loss') plt.subplot(1, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show() The output of the training proc, len(display_list), loss, loss=bce_dice_loss, mask, mask in dataset.take(num): pred_mask = model.predict(image) display([image[0, mask in test: print(img, mask in train.take(1): sample_image = img.numpy()[:, mask_val = train_test_split(img, mask) But I only got one image array and one mask array. Does it mean that there's only one image array and one mask array in the dataset? W, mask[0, metrics=[dice_loss]) model.summary() #save model save_model_path = 'tmp/weights.hdf5' cp = tf.keras.callbacks.ModelCheckpoint(filepath=save, mode='max', monitor='val_dice_loss', num_filters) encoder_pool = layers.MaxPooling2D((2, num_filters): decoder = layers.Conv2DTranspose(num_filters, num_filters): encoder = conv_block(input_tensor, num_filters): encoder = layers.Conv2D(num_filters, num=1): for image, or nan also in float32 type). I followed Google Colab and tensorflow tutorialto create the following code: from glob import glob from PIL im, outputs=[outputs]) def dice_coeff(y_true, padding='same')(decoder) decoder = layers.BatchNormalization()(decoder) decoder = layers.Activation('relu')(decoder) decoder = la, padding='same')(decoder) decoder = layers.BatchNormalization()(decoder) decoder = layers.Activation('relu')(decoder) return decod, padding='same')(encoder) encoder = layers.BatchNormalization()(encoder) encoder = layers.Activation('relu')(encoder) return encod, padding='same')(input_tensor) decoder = layers.concatenate([concat_tensor, padding='same')(input_tensor) encoder = layers.BatchNormalization()(encoder) encoder = layers.Activation('relu')(encoder) encoder, random_state=42) #load image as array and append to a list train_image = [] for m in img: img= Image.open(m) img_arr = np.array(img), sample_mask]) The output of the visualization looks normal like below: out put of the visualization #build the model train_length = len(tra, save_best_only=True) #start training history = model.fit(train_dataset, steps_per_epoch=int(np.ceil(train_length / float(batch_size))), strides=(2, test_mask): test= tf.data.Dataset.from_tensor_slices(([k], test_size=0.2, tf.newaxis] return pred_mask[0, the white part of input image and true mask is sea and it is represented by nan. If this is the problem, train_mask): train= tf.data.Dataset.from_tensor_slices(([i], val_dice, val_loss, validation_data=test_dataset, validation_steps=int(np.ceil(len(test_img) / float(batch_size))), y_pred) + dice_loss(y_true, y_pred) return loss def bce_dice_loss(y_true, y_pred) return loss model.compile(optimizer='adam', y_pred): loss = 1 - dice_coeff(y_true, y_pred): loss = losses.binary_crossentropy(y_true, y_pred): smooth = 1. # Flatten y_true_f = tf.reshape(y_true

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