我一直在尝试训练一个keras模型,但它在第一个时代的开始就一直停滞不前。最糟糕的是它没有抛出任何错误。我正在GTX 1050TI上训练
下面是我的代码示例:
import tensorflow as tf
import os
from tensorflow import keras
from keras_preprocessing.image import ImageDataGenerator
from keras_applications.xception import Xception
import matplotlib.pyplot as plt
train_dir='
你能帮我纠正这个错误吗?
TypeError: The added layer must be an instance of class Layer. Found: <keras.layers.convolutional.Conv2DTranspose object at 0x7f5dc629f240>
当我尝试执行下面这行代码时,我得到了这样的结果
decoder.add(Deconvolution2D(64, 3, 3, subsample=(1, 1), border_mode='same'))
我的导入是:
from keras.layers imp
以下代码
from tensorflow import keras
from keras.layers import Conv2D
model = keras.Sequential()
model.add(Conv2D(1, (3, 3), padding='same', input_shape=(28, 28, 1)))
执行时抛出错误:
TypeError: The added layer must be an instance of class Layer. Found: <keras.layers.convolutional.Conv2D object at 0
我是新来的。我对这个密码有问题,
#Library
import numpy as np
import pickle
import cv2
from os import listdir
from sklearn.preprocessing import LabelBinarizer
from keras.models import Sequential
from keras.layers import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
# Initialising the CNN
chars74k_classifier = Sequen
我想将卷积的值保存在变量conv1中,然后将conv1的值应用于泄漏关系激活函数中。
错误:
ValueError: Layer leaky_re_lu_1 was called with an input that isn't a symbolic tensor. Received type: <class 'keras.layers.convolutional.Conv3D'>. Full input: [<keras.layers.convolutional.Conv3D object at 0x7fc6312abe10>]. All in
我正在尝试理解CNNs,并从一个相当简单的213行数据集开始。每个类别都被分类,因此它必须适合98个类别中的6个。即使是一个简单的三层网络在50k+时代之后也不会超过20%的准确率。有什么建议吗?
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import Reshape
from
## Read the datasheet
X, Y = read_dataset()
model_path = "/Users/shalinsavalia/Desktop/ECG_CNN/CNN"
## Shuffle the dataset to mix up the rows
X, Y = shuffle(X, Y, random_state=1)
## Convert the dataset into train and test part
train_x, test_x, train_y, test_y = train_test_split(X, Y,
from tensorflow.keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers import MaxPool2D
from keras.layers.core import Activation, Flatten, Dropout, Dense
from tensorflow.keras import backend as K
impor
这是当我尝试运行它时它给我的错误:
Traceback (most recent call last):
File "/Users/kids/Library/Mail/V5/E60CBF1C-9021-4A10-8D60-06C96C141AF1/Outbox.mbox/E7C72E99-E3DB-4CDC-B1C9-15116F3478D8/Data/Attachments/405/2/ASL-Finger-Spelling-Recognition-master/main.py", line 10, in <module>
from keras.lay
我正在尝试通过使用Darknet来训练YOLO进行基于8个类的目标检测。但是,在训练过程中我收到错误消息
Wrong annotation: class_id = 4. But class_id should be [from 0 to 0], file: data/obj/images/IMG_8943.txt
IMG_8943.txt是我的一个文本文件,我在其中存储了用labelImg获得的注释。我真的不明白为什么会出现这个错误,因为我已经在配置文件中指定了类的数量:
[net]
# Testing
batch=8
subdivisions=1
# Training
batch=64
su
我有每个手势(5个手势)的训练数据700图像, ? 验证测试数据200图像 ? 和测试数据150图像。 ? 我的模型是: def get_model():
"""
Returns a compiled convolutional neural network model. Assume that the
`input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
The output layer should have `NUM_CATEGORIES` units, one for each ca
我正在尝试实现下面的python代码,但是我得到了以下错误。有人能帮我吗?
from keras.models import Sequential
from keras.constraints import maxnorm
from keras.layers.convolutional import Convolution2D
# Create the model
model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, 32, 32), activation='relu', padding=
当我试图构建这个文件时,出现了这个错误。有人知道怎么修吗?非常感谢。对不起我的英语不好。
代码:
import numpy as np
import pickle
import cv2, os
from glob import glob
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.c
autoencoder_layers.py
import theano
from keras import backend as K
from keras.backend.theano_backend import _on_gpu
from keras.layers.convolutional import Convolution2D, UpSampling2D
from keras.layers.core import Dense, Layer
from theano import tensor as T
from theano.sandbox.cuda import dnn
但是我得到了
在Keras中,我将执行以下操作来动态创建模型的层: for i in range(number_dense_layers):
model.add(layers.Dense(units=units, input_dim=input_dim,
kernel_initializer='normal', activation='relu')) 然而,在Tensorflow的情况下,我有以下几点: class generic_vns_function(tf.keras.Model):
def __init__
我正在尝试对Keras中的现有模型进行微调,以对我自己的数据集进行分类。到目前为止,我已经尝试了以下代码(摘自Keras文档:),其中的初始V3在一组新的类上进行了微调。
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras import backend as K
#
我试图让我的tensorflow模型在两类图像上进行训练,但我遇到了一个ValueError问题。有人能帮帮忙吗。相关代码如下: # Get image arrays and labels for all image files
images, labels = load_data(sys.argv[1])
# Split data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
images, labels, test_size=TEST_SIZE
)
# G