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 ..
Category : tensorflow
I’m training a tensorflow model to predict spam messages using a BiLSTM. The model is being perfectly trained with no problem. When I run the following code PAD_TYPE = ‘post’ TRUNC_TYPE = ‘post’ MAX_LEN = 98 def load_tokenizer(): with open(‘models/tokenizer.pickle’, ‘rb’) as handle: tokenizer = pickle.load(handle) return tokenizer def text_to_sequence(msg, tokenizer): tokenizer = load_tokenizer() text_sequence ..
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 ..
#!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 trying to train a fairly big (68 trainable variables) attention model with a custom training loop in tensorflow. Instead of calling the fit method, I have to resort to the tensorflow gradient tape. But the funny thing is whenever I run the calculations inside gradient tape context, the program crashes (killing the jupyter ..
enter image description here from tfx.components import CsvExampleGen from tfx.components import ExampleValidator from tfx.components import SchemaGen Source: Python..
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 trying to build a CNN model for sentiment classification. The input x is a pure text, so I have done preprocessing and vectorized it. When I try to fit the model with train dataset, it returns this error: ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=4, found ..
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 = ..
I have Unet with 4 classes, and I would like to calculate the MeanAbsoluteError like this: mae = tf.keras.losses.MeanAbsoluteError() metrics = [mae ] model.compile(optimizer=optim,loss= dice_loss_se2,metrics= metrics) the issue is this will calculate the average of the 4 classes. I’m just interested in 1 class only. So what I would like to do is to only ..
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