I’m trying to make a simple CNN classifier model. For my training images (BATCH_SIZEx227x227x1) and labels (BATCH_SIZEx7) datasets, I’m using numpy ndarrays that are fed to the model in batches via ImageDataGenerator. The loss function I’m using is tf.nn.sparse_categorical_crossentropy. The problem arises when the model tries to train; the model (batch size here is 1 for my simplified experimentations) outputs
Tag: keras
Tensorflow 2.0 – AttributeError: module ‘tensorflow’ has no attribute ‘Session’
When I am executing the command sess = tf.Session() in Tensorflow 2.0 environment, I am getting an error message as below: System Information: OS Platform and Distribution: Windows 10 Python Version: 3.7.1 Tensorflow Version: 2.0.0-alpha0 (installed with pip) Steps to reproduce: Installation: pip install –upgrade pip pip install tensorflow==2.0.0-alpha0 pip install keras pip install numpy==1.16.2 Execution: Execute command: import tensorflow
Gaierror while importing pretrained vgg model on kaggle
I am trying to import a pre-trained VGG model in keras on kaggle. I run through an gaierror which was unfamiliar. Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5 ————————————————————————— gaierror Traceback (most recent call last) /opt/conda/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args) 1317 h.request(req.get_method(), req.selector, req.data, headers, -> 1318 encode_chunked=req.has_header(‘Transfer-encoding’)) 1319 except OSError as err: # timeout error /opt/conda/lib/python3.6/http/client.py in request(self, method, url, body,
Split autoencoder on encoder and decoder keras
I am trying to create an autoencoder for: Train the model Split encoder and decoder Visualise compressed data (encoder) Use arbitrary compressed data to get the output (decoder) How to split train it and split with the trained weights? Answer Make encoder: Make decoder: Make autoencoder: Now you can use any of them any way you want to. train the
confusion matrix error “Classification metrics can’t handle a mix of multilabel-indicator and multiclass targets”
I am getting a error when I try to use confusion matrix. I am doing my first deep learning project. I am new to it. I am using the mnist dataset provided by keras. I have trained and tested my model successfully. However, when I try to use the scikit learn confusion matrix I get the error stated above. I
InvalidArgumentError: cannot compute MatMul as input #0(zero-based) was expected to be a float tensor but is a double tensor [Op:MatMul]
Can somebody explain, how does TensorFlow’s eager mode work? I am trying to build a simple regression as follows: Gradient output: [None, None, None, None, None, None] The error is following: Edit I updated my code. Now, the problem comes in gradients calculation, it is returning zero. I have checked the loss value that is non-zero. Answer Part 1: The
How to fine-tune a functional model in Keras?
Taking a pre-trained model in Keras and replacing the top classification layer to retrain the network to a new task has several examples using a Sequential model in Keras. A sequential model has methods model.pop() and model.add() which make this fairly easy. However, how is this achieved when using a functional model? This framework does not have method model.add(). How
Input Shape for 1D CNN (Keras)
I’m building a CNN using Keras, with the following Conv1D as my first layer: I’m training with the function: In which train_df is a pandas dataframe of two columns where, for each row, label is an int (0 or 1) and payload is a ndarray of floats padded with zeros/truncated to a length of 1000. The total # of training
Keras CNN Error: expected Sequence to have 3 dimensions, but got array with shape (500, 400)
I’m getting this error: ValueError: Error when checking input: expected Sequence to have 3 dimensions, but got array with shape (500, 400) These are the below codes that I’m using. Output (here I’ve 500 rows in each): Code: Any insights? Answer Two things – Conv1D layer expects input to be in the shape (batch_size, x, filters), in your case (500,400,1).
tflite: get_tensor on non-output tensors gives random values
I’m trying to debug my tflite model, that uses custom ops. I’ve found the correspondence between op names (in *.pb) and op ids (in *.tflite), and I’m doing a layer-per-layer comparison (to make sure the outputs difference are always in range 1e-4 (since it blows up at the end, I want to find the exact place where my custom layer