keras

问题 I am doing data augmentation using data_gen=image.ImageDataGenerator(rotation_range=20,width_shift_range=0.2,height_shift_range=0.2, zoom_range=0.15,horizontal_flip=False) iter=data_gen.flow(X_train,Y_train,batch_size=64) data_gen.flow() needs a rank 4 data matrix, so the shape of X_train is (60000, 28, 28, 1) . We need to pass the same shape i.e (60000, 28, 28, 1) while defining the architecture of the model as follows; model=Sequential() model.add(Dense(units=64,activation='relu',kernel

问题 I am doing data augmentation using data_gen=image.ImageDataGenerator(rotation_range=20,width_shift_range=0.2,height_shift_range=0.2, zoom_range=0.15,horizontal_flip=False) iter=data_gen.flow(X_train,Y_train,batch_size=64) data_gen.flow() needs a rank 4 data matrix, so the shape of X_train is (60000, 28, 28, 1) . We need to pass the same shape i.e (60000, 28, 28, 1) while defining the architecture of the model as follows; model=Sequential() model.add(Dense(units=64,activation='relu',kernel

问题 I am training on Google Colab with data and model weights loaded from/save to GCP. I am using Keras callbacks to save the weights to GCP. This is what the callback looks like callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath='gs://mybucket/'+ 'savename' + '_loss_{loss:.2f}', monitor='loss', verbose=1, save_weights_only=True, save_freq='epoch')] The training saves the model weights successfully to my GCP bucket, but when I try to load those weights in a new session, the cell just hangs,

问题 I am trying to build a CNN network and wuld like to probe the layer dimention using output_shape. But it's giving me an error as follows: ValueError: Input 0 is incompatible with layer conv2d_5: expected ndim=4, found ndim=2 Below is the code I am trying to execute from keras.layers import Activation model = Sequential() model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28))) print(model.output_shape) 回答1: You can check if by default the number of channels is specified at

问题 I made the following architecture Layer (type) Output Shape Param # ================================================================= embedding_7 (Embedding) (None, 50, 64) 512000 _________________________________________________________________ bidirectional_5 (Bidirection (None, 200) 132000 _________________________________________________________________ dense_9 (Dense) (None, 1) 201 ================================================================= Total params: 644,201 Trainable params:

问题 I'm trying to run my keras model using multiprocessing due to GPU OOM issue. I loaded all libraries and set up the model within the function for multiprocessing as below: When I execute this code, it gets stuck at history = q.get() , which is multiprocessing.Queue.get() . And when I remove all the code related to multiprocessing.Queue() , the code execution ends as soon as I execute the code, which I suspect that the code is not working. Even a simple print() function didn't show an output.

问题 I'm using Version: 2.1.0 of TensorFlow and 2.3.1 of keras. While importing any module of keras i'm facing below tensorflow back-end error. import pandas as pd, numpy as np, os, re, json, math, time from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasRegressor from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from sklearn.pipeline import

问题 I am creating a sklearn pipeline that consists of 3 steps: Transforms pandas dataframe into 3D array Transforms 3D array into recurrence plot (image) Trains an image classification model using Keras This is my initial data set: train_df - pandas dataframe id cycle s1 1 1 0.05 1 2 0.04 1 3 0.05 1 4 0.05 2 1 0.02 2 2 0.03 y_train array([[1., 0., 0.], [1., 0., 0.], ... [1., 0., 0.]], dtype=float32) When I run my current code (see below), I get the following error: AttributeError: 'numpy.ndarray'

问题 I'm using the module bert-for-tf2 in order to wrap BERT model as Keras layer in Tensorflow 2.0 I've followed your guide for implementing BERT model as Keras layer. I'm trying to extract embeddings from a sentence; in my case, the sentence is "Hello" I have a question about the output of the model prediction; I've written this model: model_word_embedding = tf.keras.Sequential([ tf.keras.layers.Input(shape=(4,), dtype='int32', name='input_ids'), bert_layer ]) model_word_embedding .build(input

问题 I've been working on reproducing a CNN-LSTM model for PV power forecasting from literature for the past four weeks for my Master Thesis in Energy Science (http://www.mdpi.com/2076-3417/8/8/1286). However I've been stuck on a seemingly simple issue: Any configuration of LSTM model that I've tried yields one of two things: Rediculous output, makes no sense whatsoever (flat line, complete stochasticity, negative values, you name it) Exactly the same (very believable) PV power forecast. I've done