So there is a way to load inbuilt model in tensorflow without top for transfer learning. For example: tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights=’imagenet’) But how to do the same with a h5 model saved in my pc because tensorflow.keras.models.load_model doesnt have include_top parameter? Source: Python..

#### Category : keras

I’m trying to reproduce the architecture of the network proposed in this publication in tensorFlow. Being a total beginner to this, I’ve been using this tutorial as a base to work on, using tensorflow==2.3.2. To train this network, they use a loss which implies outputs from two branches of the network at the same time, ..

I am trying to use a pre-trained model which would skip some of the layers. There are 4 layers in a model, l1->l2->l3->l4, I need to bypass the output of l2 and feed it into l4 instead of l3 during inference for instance that is bringing the activations of the second conv layer all way ..

I’m designing a CNN for mnist dataset. Then it returns an error ValueError: Negative dimension size caused by subtracting 3 from 2 for ‘{{node conv2d_23/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](batch_normalization_19/cond/Identity, conv2d_23/Conv2D/ReadVariableOp)’ with input shapes: [?,2,2,12], [3,3,12,12]. I tried to change padding = ‘valid’ to padding = ..

I need to make a model that has 2 dropout layers and two LSTM layers. Unfortunately I have a problem with input shape that goes to my second LSTM layer. After searching for the problem I found out I need to change the input dimensions but I don’t know how to do that. I found ..

I want to install tensorflow (and Keras too) for R. I used thoses lines install.packages("keras") install.packages("tensorflow") library(keras) library(tensorflow) And now I want to do : install_tensorflow() install_keras() But my python environement is inconsistent. Is there a way to told R to look for virtual python environnement ? Thanks Source: Python..

I am facing this issue while training the model. Everything seems fine but I cannot understand the problem. This is the error I am facing: InvalidArgumentError: Matrix size-incompatible: In[0]: [32,29], In[1]: [128,1] [[node gradient_tape/sequential_11/dense_23/MatMul (defined at <ipython-input-89-9b0f878131ef>:2) ]] [Op:__inference_train_function_10031] Function call stack: train_function Here is the model summary: Model: "sequential_11" _________________________________________________________________ Layer (type) Output Shape ..

So, Here i have a train data(x)[float32] which is the product of MFCC then i have the target(y)[int32] which i already encode it. i already giving them a padding each of (x) and (y). Here is the Shape : Train Data Shape (MFCC): (1740, 107, 16) Target Train Data Shape (Encoded) : (1740, 107) Test ..

I am working on a computer vision project, for input i have 6 classes each containing gray images with otsu’s thresholding method applied of size (224,224) and i have trained the model and stored it in RecogModel.tfl file here is the model i am using. model = Sequential() # 1st Convolutional Layer model.add(Conv2D(filters=96, input_shape=(224,224,1), kernel_size=(11,11),strides=(4,4), ..

I am trying to apply data augmentation for a binary image classification problem in the following way as mentioned in tensorflow docs: https://www.tensorflow.org/tutorials/images/classification#data_augmentation My model is this: Sequential([ data_augmentation, layers.experimental.preprocessing.Rescaling(1./255), layers.Conv2D(16, 3, padding=’same’, activation=’relu’), layers.MaxPooling2D(), layers.Dropout(0.2), layers.Conv2D(32, 3, padding=’same’, activation=’relu’), layers.MaxPooling2D(), layers.Dropout(0.2), layers.Conv2D(64, 3, padding=’same’, activation=’relu’), layers.MaxPooling2D(), layers.Flatten(), layers.Dense(128, activation=’relu’), layers.Dropout(0.5), layers.Dense(1, activation=’sigmoid’) ]) When ..

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