Category : tensorflow

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 ..

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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 ..

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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 ..

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#!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 ..

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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, ..

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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 = ..

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