Tensorboard (used via keras) only displays images for 3 filters per layer of my convnet. What is happening?

I am training a simple convolutional neural net with Keras and calling tensorboard to visualize the learning process. Under the Images tab I can see the images of the biases and weights for each layer, but only 3 images are displayed for each layer even though my network uses 32 filters for both of its conv layers. is there any way to fix this? The code and a screenshot of the problem are shown below

Screenshot of the images tab showing only 3 images

Here is the code I use:

    ########### create model model=Sequential() # first layer is convolutional layer with 61*585/600 as input and ReLu activation model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(61, 540, 1), W_regularizer=l2(0.08))) model.add(Dropout(0.25)) # Second layer is maxpool layer model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(Dropout(0.25)) # third layer is FC layer 8 neurons model.add(Flatten()) ##model.add(Dense(20, activation='relu')) ##model.add(Dense(10, activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dropout(0.25)) # last layer is FC layer 2 neurons model.add(Dense(2, activation='softmax'))   #compile model with gradient descent sgd = SGD(lr=0.005, decay=0, momentum=0, nesterov=False) ##AD=Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])  # add a callback to stop when the model stops improving #early_stp = EarlyStopping(monitor='acc', min_delta=0.0002,                              # patience=10, baseline=1.0)   # Create a TensorBoard instance with the path to the logs directory tensorboard = TensorBoard(log_dir='logs/{}'.format(time()), histogram_freq=1, write_graph=True, write_images=True) log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard = TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True, write_images=True) #fit the model model.fit(X, Y, batch_size=50, epochs=100, callbacks=[ tensorboard], validation_data=(Xtest,Ytest)) 

Any help will be greatly appreciated!