tensorflow

问题 while i was opening and decoding an image from python with tensorflow i got an error %tensorflow_version 2.x import tensorflow as tf from PIL import Image import requests from io import BytesIO response = requests.get(r'https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png') img = Image.open(BytesIO(response.content)) tf_image = tf.io.read_file(img) error is hapenning when i used tf.io.read_file(img) Throws error of

问题 I was looking through the architecture of EfficientnetB0 and noticed DepthwiseConv2D operation. Did some digging and found that there's also a SeparableConv2D. What exactly are these operations? 来源： https://stackoverflow.com/questions/61967172/what-is-depthwiseconv2d-and-separableconv2d-how-is-it-different-from-normal-con

问题 I was looking through the architecture of EfficientnetB0 and noticed DepthwiseConv2D operation. Did some digging and found that there's also a SeparableConv2D. What exactly are these operations? 来源： https://stackoverflow.com/questions/61967172/what-is-depthwiseconv2d-and-separableconv2d-how-is-it-different-from-normal-con

问题 After training a model using Google Colab, I downloaded it using the following command (inside Google Colab): model.save('model.h5') from google.colab import files files.download('model.h5') My problem is that when I try to load the downloaded model.h5 using my local machine (outside Google Colab), I get the following error: [input] from keras.models import load_model model = load_model(model.h5) [output] Traceback (most recent call last): File "test.py", line 2, in <module> model = load

问题 After training a model using Google Colab, I downloaded it using the following command (inside Google Colab): model.save('model.h5') from google.colab import files files.download('model.h5') My problem is that when I try to load the downloaded model.h5 using my local machine (outside Google Colab), I get the following error: [input] from keras.models import load_model model = load_model(model.h5) [output] Traceback (most recent call last): File "test.py", line 2, in <module> model = load

问题 I'm very new to TensorFlow 2.0. I wrote a code for Cyclic GAN as follows (I extract code only for building generator neural network): def instance_norm(x, epsilon=1e-5): scale = tf.Variable(initial_value=np.random.normal(1., 0.02, x.shape[-1:]), trainable=True, name='SCALE', dtype=tf.float32) offset = tf.Variable(initial_value=np.zeros(x.shape[-1:]), trainable=True, name='OFFSET', dtype=tf.float32) mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True) inv = tf.math.rsqrt(variance +

问题 I have a custom neural network written in Tensorflow.Keras and apply the hard-swish function as activation (as used in the MobileNetV3 paper): Implementation: def swish(x): return x * tf.nn.relu6(x+3) / 6 I am running quantization aware training and write a protobuf file at the end. Then, I am using this code to convert to tflite (and deploy it finally on the EdgeTPU): tflite_convert --output_file test.tflite --graph_def_file=test.pb --inference_type=QUANTIZED_UINT8 --input_arrays=input_1 -

问题 I have a custom neural network written in Tensorflow.Keras and apply the hard-swish function as activation (as used in the MobileNetV3 paper): Implementation: def swish(x): return x * tf.nn.relu6(x+3) / 6 I am running quantization aware training and write a protobuf file at the end. Then, I am using this code to convert to tflite (and deploy it finally on the EdgeTPU): tflite_convert --output_file test.tflite --graph_def_file=test.pb --inference_type=QUANTIZED_UINT8 --input_arrays=input_1 -

问题 I am trying to run this statement in MacOS. conda install -c conda-forge tensorflow It just stuck at the Solving Environment: Never finish. $ conda --version conda 4.5.12 回答1: On win10 I waited about 5-6 minutes but it depends of the number of installed python packages and your internet connection. Also you can install it via Anaconda Navigator 回答2: Nothing worked untill i ran this in conda terminal: conda upgrade conda Note that this was for poppler (conda install -c conda-forge poppler) 回答3

问题 If I use tf.transpose does it also change the memory layout? In numpy it is used the function np.ascontiguousarray . I mean this would be important if I use cuda. Because it makes a difference if the memory layout is [N C H W] or [N H W C]. (N ... Nr of samples, H ... array height, W ... array width, C ... array depth, e.g. RGB) How to check this? 回答1: If you read carefully the documentation you can find the answer: Numpy Compatibility In numpy transposes are memory-efficient constant time