im trying to implement this function so that it uses the Inception V1 model on tensorflow hub to obtain the three top ImageNet labels for a given image, along with their probabilities. with open("local/data/ImageNetLabels.txt", "r") as f: labels_list = [i.rstrip() for i in f.readlines()] def get_top3_inceptionv1_labels(img, labels_list): from numpy import exp from skimage.transform import resize ..
Im asking myself does the following code do only one step of gradient descent or does it do the whole gradient descent algorithm? opt = tf.keras.optimizers.SGD(learning_rate=self.learning_rate) opt = tf.keras.optimizers.SGD(learning_rate=self.learning_rate) train = opt.minimize(self.loss, var_list=[self.W1, self.b1, self.W2, self.b2, self.W3, self.b3]) You need to do a number of steps in gradient descent which you determine. But Im not ..
display_instance() function shows only two masks even if there are multiple masks in the data set. Can be seen with mask.shape[-1]. How to be sure all the masks are being used or properly loaded for the training? code used to visualize Source: Python-3x..
I’m trying to optimize this function for a tetrahedral color space transform with a ANN. There are 21 parameters that represent the 3 RGB coordinates of the 7 corners of the RGB color space. The tetrahedral transform can align the corners to match one camera color space to another in a piece-wise linear fashion. I’d ..
I tried to follow these instructions https://github.com/wmcnally/deep-darts because i want to test DeepDarts for a Dart-Counter. It’s my first time with Anaconda, also the first time with Python. After Step 4, i got this error message: ERROR: THESE PACKAGES DO NOT MATCH THE HASHES FROM THE REQUIREMENTS FILE. If you have updated the package versions, ..
I’m working on regressing bounding boxes on images. Therefore I’d like to define a loss function that gives a higher penalty if the predicted values are outside of the bounding box. The array y_true looks like as follows: y_true = [x_tl, y_tl, x_br, y_br] the first dimension is defined by the batch size. def customLoss(y_true, ..
#!pip install tensorflow-addons import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import tensorflow_addons as tfa My dataset is just a Parent folder with the name ‘dataset’ which has the ‘Test’ folder containing the test set images and ‘training’ folder which has the training images. In addition to ..
I am very new at python and machine learning.. please do not give minus my question I really need to help. I have emotion recognition codes and predict faces, I need to create its accuracy and loss graphs. every articles say that I need to add code below for graphs model.compile(loss=’mse’, optimizer=’sgd’, metrics=[‘accuracy’]) model.fit(x, y) ..
Im trying to train the dataset using keras and tensorflow, the code runs fine till the model summary, after that im getting value error here my code for training the params……. ….. … opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS) model.compile(loss="binary_crossentropy", optimizer=opt,metrics=["accuracy"]) print("[INFO] training network…") history = model.fit_generator( aug.flow(x_train, y_train, batch_size=BS), validation_data=(x_test, y_test), steps_per_epoch=len(x_train) // BS, ..
I am working on an ESPCN model and I tried to build it like this, upscale_factor = 3 inputs = keras.Input(shape=(None, None, 1)) conv1 = layers.Conv2D(64, 5, activation="tanh", padding="same")(inputs) conv2 = layers.Conv2D(32, 3, activation="tanh", padding="same")(conv1) conv3 = layers.Conv2D((upscale_factor*upscale_factor), 3, activation="sigmoid", padding="same")(conv2) outputs = tf.nn.depth_to_space(conv3, upscale_factor, data_format=’NHWC’) model = Model(inputs=inputs, outputs=outputs) def gen_dataset(filenames, scale): crop_size_lr = ..