这是我的模特。
# Import Keras
import tensorflow as tf
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Flatten,GlobalMaxPool2D
from tensorflow.python.keras.layers import Input, LSTM, Embedding, Dense
from tensorflow.python.keras.models import Model, Sequential
from tensorflow.python import keras
# Define CNN for Image Input
vision_model = Sequential()
vision_model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(120, 160, 3)))
vision_model.add(Conv2D(64, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(128, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(196, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(196, (3, 3), activation='relu'))
vision_model.add(Conv2D(196, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(384, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(384, (3, 3), activation='relu'))
vision_model.add(GlobalMaxPool2D())
vision_model.summary()
image_input = Input(shape=(120, 160, 3))
encoded_image = vision_model(image_input)
# Define RNN for language input
question_input = Input(shape=(42,), dtype='int32')
embedded_question = Embedding(input_dim=500, output_dim=256, input_length=100)(question_input)
encoded_question = LSTM(256,return_sequences=True)(embedded_question)
encoded_question = LSTM(256)(embedded_question)
# Combine CNN and RNN to create the final model
merged = keras.layers.concatenate([encoded_question, encoded_image])
output = Dense(26, activation='softmax')(merged)
vqa_model = Model(inputs=[image_input, question_input], outputs=output)
vqa_model.summary()
。 。 。
vqa_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])
vqa_model.fit_generator(train_gen,len(train_data[0])//16,validation_data=val_gen,validation_steps=split//16,verbose=1,epochs=100)
运行此命令后出现此错误
UnknownError：无法获取卷积算法。这可能是因为cuDNN初始化失败，所以请尝试查看上面是否打印了警告日志消息。 [[{{节点顺序/ conv2d / Conv2D}} = Conv2D [T = DT_FLOAT，_class = [“ loc：@ training / Adam / gradients / sequential / conv2d / Conv2D_grad / Conv2DBackpropFilter”]，data_format =“ NCHW”，膨胀= [1，1，1，1]，padding =“ SAME”，步幅= [1，1，1，1]，use_cudnn_on_gpu = true，_device =“ / job：localhost /副本：0 /任务：0 /设备： GPU：0“]（训练/亚当/梯度/顺序/ conv2d / Conv2D_grad / Conv2DBackpropFilter-0-TransposeNHWCToNCHW-LayoutOptimizer，顺序/ conv2d / Conv2D / ReadVariableOp）]] [[{{node ConstantFoldingCtrl / loss / dense_loss / broadcast_weights / assert_broadcastable / AssertGuard / Switch_0 / _316}} = _Recvclient_terminated = false，recv_device =“ / job：localhost /副本0 /任务：0 /设备：CPU：0”， send_device =“ / job：localhost /副本：0 / task：0 / device：GPU：0”，send_device_incarnation = 1，tensor_name =“ edge_1886 _... d / Switch_0”，tensor_type = DT_FLOAT，_device =“ / job：localhost / replica：0 / task：0 / device：CPU：0“]] “
我在使用NVIDIA 1050Ti的本地系统上使用tensorflow gpu。
我试图搜索解决方案，但是没有用。