keras

问题 I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did: # Start With First Branch ############################################################ branch_one = Sequential() # Adding The Convolution branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'

问题 I'm training a semantic segmentation model using Keras with TensorFlow backend. I adopted ImageDataGenerator to do the image augmentation, including rotation, flip and shift. By following the documentation, I created a dictionary maskgen_args and used it as arguments to instantiate two ImageDataGenerator instances. maskgen_args = dict( rotation_range=90, validation_split=VALIDATION_SPLIT ) image_datagen = ImageDataGenerator(**maskgen_args) mask_datagen = ImageDataGenerator(**maskgen_args) The

问题 I've seen that this question has been asked a few times before, but without any resolution. My problem is simple: I would like to implement a loss function which computes the MSE between the gradient of the prediction and the truth value (eventually moving on to much more complicated loss functions). I define the following two functions: def my_loss(y_true, y_pred, x): dydx = K.gradients(y_pred, x) return K.mean(K.square(dydx - y_true), axis=-1) def my_loss_function(x): def gradLoss(y_true, y

问题 I've seen that this question has been asked a few times before, but without any resolution. My problem is simple: I would like to implement a loss function which computes the MSE between the gradient of the prediction and the truth value (eventually moving on to much more complicated loss functions). I define the following two functions: def my_loss(y_true, y_pred, x): dydx = K.gradients(y_pred, x) return K.mean(K.square(dydx - y_true), axis=-1) def my_loss_function(x): def gradLoss(y_true, y

问题 I am newbie to keras. I ran it on a dataset where my objective was to reduce the logloss. For every epoch it is giving me the same loss value. I am confused whether i am on the right track or not. For example: Epoch 1/5 91456/91456 [==============================] - 142s - loss: 3.8019 - val_loss: 3.8278 Epoch 2/5 91456/91456 [==============================] - 139s - loss: 3.8019 - val_loss: 3.8278 Epoch 3/5 91456/91456 [==============================] - 143s - loss: 3.8019 - val_loss: 3.8278

问题 While training the yolov3 framework, there's always this module error I have tried reinstalling keras and tensorflow, and the version of keras is 2.3.0 and the version of tensorflow is 1.14.0. Traceback (most recent call last): File "train.py", line 6, in <module> import keras.backend as K File "F:\Anacoda\lib\site-packages\keras\__init__.py", line 3, in <module> from . import utils File "F:\Anacoda\lib\site-packages\keras\utils\__init__.py", line 27, in <module> from .multi_gpu_utils import

问题 While training the yolov3 framework, there's always this module error I have tried reinstalling keras and tensorflow, and the version of keras is 2.3.0 and the version of tensorflow is 1.14.0. Traceback (most recent call last): File "train.py", line 6, in <module> import keras.backend as K File "F:\Anacoda\lib\site-packages\keras\__init__.py", line 3, in <module> from . import utils File "F:\Anacoda\lib\site-packages\keras\utils\__init__.py", line 27, in <module> from .multi_gpu_utils import

问题 I am trying to create a neural network with 1 input and 2 outputs: This is my code for predicting: def predict(self, environment): policy, value = self.AI.predict([environment]) print(type(policy), type(value)) print(policy, value) and this is what is printed on the screen: <class 'numpy.ndarray'> <class 'numpy.ndarray'> [[0.01186783 0.09048636 0.3038044 0.02231415 0.20798717 0.24917272 0.06396621 0.02610502 0.02429625]] [] Why is the value array empty? Shouldn't it have 1 float? This is how

问题 I am trying to create a neural network with 1 input and 2 outputs: This is my code for predicting: def predict(self, environment): policy, value = self.AI.predict([environment]) print(type(policy), type(value)) print(policy, value) and this is what is printed on the screen: <class 'numpy.ndarray'> <class 'numpy.ndarray'> [[0.01186783 0.09048636 0.3038044 0.02231415 0.20798717 0.24917272 0.06396621 0.02610502 0.02429625]] [] Why is the value array empty? Shouldn't it have 1 float? This is how

问题 I'm trying to implement something like if np.max(subgrid) == np.min(subgrid): middle_middle = cur_subgrid + 1 else: middle_middle = cur_subgrid Since the condition can only be determined at run-time, I'm using Keras syntax as following middle_middle = K.switch(K.max(subgrid) == K.min(subgrid), lambda: tf.add(cur_subgrid,1), lambda: cur_subgrid) But I'm getting this error: <ipython-input-112-0504ce070e71> in col_loop(j, gray_map, mask_A) 56 57 ---> 58 middle_middle = K.switch(K.max(subgrid) ==