Tag: loss=’sparse_categorical_crossentropy’

R Keras – multiclass classification accuracy and validation stuck at near zero

Post author By Full Stack
Post date November 19, 2020
No Comments on R Keras – multiclass classification accuracy and validation stuck at near zero

I am teaching myself the keras framework and I am trying to train a neural network on the IMDBWIKI faces data set using R. I configured a python environment with python3.8 and keras 2.4.3 and tensor flow 2.3.1 and other prerequisites. I went through a series of steps pre-processing the "mat" files provided with both […]

Tags "age_class")) %>% mutate(age = as.integer(age)) trainy % select(age) %>% as.data.frame(.) %>% mutate(age = as.inte, "nm0106844_rm841063168_1963-5-29_1973.jpg", "nm0272706_rm399017216_1994-10-9_2004.jpg", "nm1216253_rm549034752_1989-2-15_1999.jpg"), "nm1415323_rm1815778816_1992-11-23_2002.jpg", "nm1628077_rm1138526464_1999-4-27_2009.jpg", ) history % fit( my_train_pics, {epochs: 100, 0L, 1))) %>% `colnames<-` (c("age", 10, 128)) img = image_to_array(image) return(img) } image_paths = list.files(trainDir, 6L, activation = "relu") %>% layer_dense(units = nrow(age_mapping), activation = "softmax") my_model %>% compile( optimizer ="adam", activation: 'relu', age = c("10", age) %>% sample_frac(.9) testingData <- anti_join(meta_prepoc, batch size and epochs with no luck): my_model % layer_dense(units =512, batch_size = 32, by = c("age")) %>% pull(age_class) Next, class = "data.frame") I use dplyr's group_by, full_path = c("nm1477924_rm1835773952_1995-12-3_2005.jpg", full.names = TRUE) my_train_pics = lapply(image_paths, gender = c(0L, However, I am teaching myself the keras framework and I am trying to train a neural network on the IMDBWIKI faces data set using R. I configured a pyt, I convert them to arrays, I load the images into three subsets, image_preprocess) my_train_pics <- do.call(rbind, input_shape = c(ncol(my_train_pics))) %>% layer_dense(units =256, loss='sparse_categorical_crossentropy', metrics=c("accuracy"), my_train_pics) my_train_pics <- my_train_pics/255 I am trying to wrap my head around these matrices/images/tensors and I think I am screwing, n = 1, recursive = TRUE, resize and rescale - here's my code for the training subset: image_preprocess <- function(img_path) { img = image_load(img_path, roughly 20K images that I would like to use to train a simple model to predict age. Here's some sample data: structure(list(n = 1:6, row.names = c(NA, sample_frac and anti_join to create subsets of training, target_size = c(128, they seem to have issues with model specification which I believe isn't the case here. Am not looking for a perfect model but being stuck at, trainingData) And validation data follows the same pattern. I then create three vectors of labels (train, trainY, validation and testing subsets of images to address class imbalance. In fact in my pre-processing I intentionally drop many images where we d, validation and testing) where I map ages to a list starting at 0 and through the max of ages; this is consistent through the three subsets (k, validation_data = list(my_valid_pics, validy) ) I'd appreciate if anyone can point me in the right direction - I've read many other posts on SO

Mastering Development

Boolean Labels Training with Keras causes the error “Please provide data which shares the same first dimension”

Post author By Full Stack
Post date November 18, 2020
No Comments on Boolean Labels Training with Keras causes the error “Please provide data which shares the same first dimension”

So, here is what I am trying to do. My model has to receive a number of training samples, each a conjunction of Boolean literals (i.e. a vector of 0 or 1s) assigned with a truth value. Learning from the samples, it must be able to receive some test vector and determine its truth value. […]

Tags ..., ), 0, 1) The rest of the code is as follows: import numpy as np from random import randint from sklearn.utils import shuf, 1). Then there is a training label array containing labels for each sample. That is, 1)) #-------- train_labels, 1)) model = Sequential([ Dense(units=16, 1)) scaled_train_samples = scaler.fit_transform(train_samples.reshape(-1, 1] may be either acceptable or not (labeled with 1 or 0.) My training sample array contains 15202 of such vectors. It is an array of size (15, 20) (15202, 20) and the train label array is of size (15202, 20)) train_labels = np.random.randint(2, a vector of 0 and 1s such as V = [1, activation='relu', activation='softmax') ]) model.compile(optimizer=Adam(learning_rate=0.0001), and how can I fix that? Thanks., batch_size=10, Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.metrics import categorical_crossentropy #Main Code # Randomly genera, Dense(units=2, Dense(units=32, each a conjunction of Boolean literals (i.e. a vector of 0 or 1s) assigned with a truth value. Learning from the samples, epochs=30, for illustration only train_samples = np.random.randint(2, here is what I am trying to do. My model has to receive a number of training samples, input_shape[1]), it must be able to receive some test vector and determine its truth value. More concretely, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x=scaled_train_samples, size=(15202, So, the following piece of code print(np.shape(train_samples)) print(type(train_samples)) print(np.shape(train_labels)) print(type(train_labels), train_samples = shuffle(train_labels, train_samples) scaler = MinMaxScaler(feature_range=(0, verbose=2) The final line causes an error: ValueError: Data cardinality is ambiguous: x sizes: 304040 y sizes: 15202 Please provide dat, y=train_labels

Artificial Intelligence (AI) Mastering Development

How can I create a network that takes a single digit as an input and outputs a 2D array?

Post author By Jessica Alba
Post date August 20, 2020
No Comments on How can I create a network that takes a single digit as an input and outputs a 2D array?

I want to create a network that can take a single digit (float) and then output a 2D array. I’m using TensorFlow in Python and so far I have this for my model and training. i = Input(shape=(1,)) x = Dense(512, activation=’relu’)(i) x = Dropout(0.2)(x) x = Dense(K, activation=’softmax’)(x) model = Model(i, x) model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, […]

Tags )) x = Dense(512, 2] and labels shape [1920000] [[node loss/dense_30_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined, activation='relu')(i) x = Dropout(0.2)(x) x = Dense(K, activation='softmax')(x) model = Model(i, epochs=5) x_train is a 1D np.array of numbers and y_train is an array of 2D np.array. There are as many entries in x_train as there are 2D a, got logits shape [3, I want to create a network that can take a single digit (float) and then output a 2D array. I'm using TensorFlow in Python and so far I have, loss='sparse_categorical_crossentropy', metrics=['accuracy']) r = model.fit(x_train, x) model.compile(optimizer='adam', y_train

Mastering Development

How to stop Keras from sharing weights/layers between different models?

Post author By Full Stack
Post date August 10, 2020
No Comments on How to stop Keras from sharing weights/layers between different models?

I am using 10fold-validation to assess how well the model generalizes for unseen data. For that I have to create multiple models with the same layers structure and compile params. When the 10fold-validation process starts, I noticed that multiple models use shared weights/layers while I want to train the different model all over again ( […]

Tags (train_idxs, ), {models, activation='sigmoid', activation='softmax') }) model.compile( optimizer=Adam(learning_rate=0.0001), dataset_labels, dataset_labels = get_dataset() # helper function that just fetches the dataset from csv file scores = kfold_validation(dataset_samples, Dense(units=12, Dense(units=9, epochs=20, I am using 10fold-validation to assess how well the model generalizes for unseen data. For that I have to create multiple models with the sam, I noticed that multiple models use shared weights/layers while I want to train the different model all over again ( without the shared weight, input_shape=(6, kfold) print(scores) kfold() kfold_validation.py from sklearn.model_selection import StratifiedKFold def kfold_validation(x_data, kfold): scores = [] kfold_list = StratifiedKFold(n_splits=kfold, loss='sparse_categorical_crossentropy', metrics=sparse_categorical_accuracy ) models_list.append(model) dataset_samples, models_list, shuffle=True, shuffle=True) for idx, val_idxs) in enumerate(kfold_list.split(x_data, validation_data=(x_data[val_idxs], verbose=0) scores.append(score) return scores RESULT LOGS OF 3 FOLD ITERATIONS (WHICH MEANS OF 3 DIFFERENT MODELS) OF 5 EPOCHS, verbose=2) score = model.evaluate(x_data[val_idxs], y_data, y_data)): model = models[idx] model.fit(x_data[train_idxs], y_data[train_idxs], y_data[val_idxs])

Artificial Intelligence (AI) Mastering Development

Flattened input layer shape

Post author By Jessica Alba
Post date July 26, 2020
No Comments on Flattened input layer shape

The code below was taken from TensorFlow in Practice by deeplearning.ai course in Coursera (computer vision example – week 2). import tensorflow as tf mnist = tf.keras.datasets.fashion_mnist (training_images, training_labels), (test_images, test_labels) = mnist.load_data() training_images = training_images / 255.0 test_images = test_images / 255.0 model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation=tf.nn.relu), tf.keras.layers.Dense(10, activation=tf.nn.softmax)]) model.compile(optimizer=tf.optimizers.Adam(), loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’]) print("Executing Training:") […]

Tags (test_images, activation=tf.nn.relu), activation=tf.nn.softmax)]) model.compile(optimizer=tf.optimizers.Adam(), Am I missing something here ?, epochs=5) print("Executing inference:") model.evaluate(test_images, loss='sparse_categorical_crossentropy', metrics=['accuracy']) print("Executing Training:") model.fit(training_images, test_labels) = mnist.load_data() training_images = training_images / 255.0 test_images = test_images / 255.0 model = tf.keras.models.Sequen, test_labels) The question: How TensorFlow deduce the shape of the input layer? Which shape is being flattened here? The input shape should b, tf.keras.layers.Dense(10, tf.keras.layers.Dense(128, The code below was taken from TensorFlow in Practice by deeplearning.ai course in Coursera (computer vision example - week 2). import tensor, training_labels)

Mastering Development

Estimator should be an estimator implementing ‘fit’ method – error

Post author By Full Stack
Post date July 20, 2020
No Comments on Estimator should be an estimator implementing ‘fit’ method – error

I am just trying to implement Cross Validation to MLP but this error doesn’t let me go model = keras.Sequential([ keras.layers.Dense(150,input_dim=21, activation=’relu’), keras.layers.Dense(100, activation=’relu’), keras.layers.Dense(50, activation=’relu’), keras.layers.Dense(24,activation=’softmax’) ]) model.compile(optimizer=opt2, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’]) MLP = model.fit(X_train, Y_train,epochs=20) scores_ = cross_val_score(model, X, y, cv=5) print("scores = ",scores_)

Tags activation='relu', activation='softmax') ]) model.compile(optimizer=opt2, cv=5) print("scores = ", epochs=20) scores_ = cross_val_score(model, I am just trying to implement Cross Validation to MLP but this error doesn't let me go model = keras.Sequential([ keras.layers.Dense(150, input_dim=21, keras.layers.Dense(100, keras.layers.Dense(24, keras.layers.Dense(50, loss='sparse_categorical_crossentropy', metrics=['accuracy']) MLP = model.fit(X_train, scores_), X, y, y_train

Artificial Intelligence (AI) Mastering Development

CIFAR-10 can’t get above 10% Accuracy with MobileNet, VGG16 and ResNet on Keras

Post author By Jessica Alba
Post date July 17, 2020
No Comments on CIFAR-10 can’t get above 10% Accuracy with MobileNet, VGG16 and ResNet on Keras

I’m trying to train the most popular Models (mobileNet, VGG16, ResNet…) with the CIFAR10-dataset but the accuracy can’t get above 9,9%. I want to do that with the completely model (include_top=True) and without the weights from imagenet. I have tried increasing/decreasing dropout and learning rate and I changed the optimizers but I become always the […]

Tags -32, 3, 9%. I want to do that with the completely model (include_top=True) and without the weights from imagenet. I have tried increasing/decreasing, amsgrad=False) model_1.compile(optimizer=adam, batch_size=batch_size, beta_1=0.9, beta_2=0.999, classes=y_train.shape[1]) batch_size= 100 epochs=50 learn_rate=.001 sgd=SGD(lr=learn_rate, decay=0.0, epochs=epochs, epsilon=None, I'm trying to train the most popular Models (mobileNet, input_shape=(32, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model_1.fit_generator(train_generator.flow(x_train, momentum=.9), nesterov=False) adam=Adam(lr=learn_rate, ResNet...) with the CIFAR10-dataset but the accuracy can't get above 9, steps_per_epoch=x_train.shape[0]//batch_size, that the layers of those Models are not set to be trainable? base_model_1 = MobileNet(include_top=True, validation_data=val_generator.flow(x_val, validation_steps=250, verbose=1) Results of MobileNet: Epoch 1/50 350/350 [==============================] - 17s 50ms/step - loss: nan - accuracy: 0.0990 - v, VGG16, weights=None, y_train, y_val)

Mastering Development

Model-building function did not return a valid Keras Model instance, found

Post author By Full Stack
Post date June 1, 2020
No Comments on Model-building function did not return a valid Keras Model instance, found

Hi i am trying to learn CNN following Krish naik Youtube tutorial in one program i am getting that error please help me my code is given below import keras.datasets fashion=keras.datasets.fashion_mnist (x_train,y_train),(x_test,y_test)=fashion.load_data() x_train=x_train/255.00 x_test=x_test/255.00 x_train=x_train.reshape(x_train.shape[0],x_train.shape[1],x_train.shape[2],1) x_test=x_test.reshape(x_test.shape[0],x_test.shape[1],x_test.shape[2],1) from keras.models import Sequential from keras.layers import Conv2D,Flatten,Dropout,Dense from keras.optimizers import Adam def build_knn(hp): models=Sequential() models.add(Conv2D(filters=hp.Int(‘conv2d_1’,min_value=32,max_value=128,step=16), kernel_size=hp.Choice(‘conv1_kernal’,values=[3,5]), activation=’relu’, input_shape=(28,28,1) […]

Tags 1) )) models.add(Conv2D(filters=hp.Int('conv2d_1', 1) from keras.models import Sequential from keras.layers import Conv2D, 1) x_test=x_test.reshape(x_test.shape[0], 1e-3, 28, 5, activation='relu', activation='relu' )) models.add(Flatten()) models.add(Dense(hp.Int('neural1', activation='relu')) models.add(Dense(10, activation='softmax')) models.compile(optimizer=Adam(hp.Choice('learning_rate', Dense from keras.optimizers import Adam def build_knn(hp): models=Sequential() models.add(Conv2D(filters=hp.Int('conv2d_1', directory='jupyterfiles', Dropout, Flatten, Hi i am trying to learn CNN following Krish naik Youtube tutorial in one program i am getting that error please help me my code is given belo, input_shape=(28, kernel_size=hp.Choice('conv1_kernal', loss='sparse_categorical_crossentropy', max_trials=5, max_value=128, metrics=['accuracy'] ) return models from kerastuner import RandomSearch from kerastuner.engine.hyperparameters import H, min_value=32, objective='val_accuracy', project_name='krish_naik_fashion_mnist') please help me thanks in advance, step=16), values=[1e-2, values=[3, x_test, X_test.shape[1], x_test.shape[2], X_train.shape[1], X_train.shape2, y_test)=fashion.load_data() x_train=x_train/255.00 x_test=x_test/255.00 x_train=x_train.reshape(x_train.shape[0], y_train

Mastering Development

Multiple output values on tensorflow [closed]

Post author By Full Stack
Post date March 15, 2020
No Comments on Multiple output values on tensorflow [closed]

I want to make a tensorflow model that would return me 14400 values. I have input of shape (1, 7854) and output of shape (1, 14400). Using this data I get error: ValueError: Can not squeeze dim[1], expected a dimension of 1, got 14400 for ‘metrics/accuracy/Squeeze’ (op: ‘Squeeze’) with input shapes: [?,14400]. My code is: […]

Tags )), 1) and it worked, 14400) model.fit(train_data, 14400)?, 14400). Using this data I get error: ValueError: Can not squeeze dim[1], 14400]. My code is: model = tf.keras.models.Sequential([ tf.keras.layers.Dense(10000, 7854) and output of shape (1, 7854) output = ... # numpy array of shape (1, activation=tf.nn.relu), activation=tf.nn.relu) ]) model.compile(optimizer='adam', epochs=10) I've tested this code on output shape of (1, expected a dimension of 1, got 14400 for 'metrics/accuracy/Squeeze' (op: 'Squeeze') with input shapes: [?, I want to make a tensorflow model that would return me 14400 values. I have input of shape (1, input_shape=(7854, loss='sparse_categorical_crossentropy', metrics=['accuracy']) train_data = ... # numpy array of shape (1, Output, so my question is how to make the model work on output of shape (1, tf.keras.layers.Dense(10000

Artificial Intelligence (AI) Development

Trouble with EarlyStopping in tf.keras

Post author By Jessica Alba
Post date March 11, 2020
No Comments on Trouble with EarlyStopping in tf.keras

I am training my first transferred learning model(yay!) and I am having trouble getting the model to stop training when the validation loss hasn’t changed by more than 0.1 in more than 3 epochs. Here is the relevant block of code early_stopping = tf.keras.callbacks.EarlyStopping(monitor=’val_loss’, patience=3, min_delta = 0.1) model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’], callbacks=[early_stopping]) EPOCHS = 100 […]

Tags callbacks=[early_stopping]) EPOCHS = 100 history = model.fit(training_batches, can I get a link to a tutorial?, epochs=epochs, I am training my first transferred learning model(yay!) and I am having trouble getting the model to stop training when the validation loss h, loss='sparse_categorical_crossentropy', metrics=['accuracy', min_delta = 0.1) model.compile(optimizer='adam', patience=3, validation_data=validation_batches) And here are some of the logs: Epoch 32/100 155/155 [==============================] - 21s 134m