Keras TensorBoard visulize Conv Kernels

I am using Keras with TensorFlow as backend.
Now i want to use the TensorBoard callback to visualize my conv layer kernels.
But i can only see the first conv layer kernel in TensorBoard and my Dense layers at the end.
For the other conv layers i can just the the bias values and not the kernels.

Here is my sample code for the Keras model.

tb = TensorBoard(

    log_dir=log_dir, 

    histogram_freq=epochs, 

    write_images=True)



# Define the DNN

model = Sequential()

model.add(Conv2D(filters=16, kernel_size=3, input_shape=(width, height, depth), name="conv1"))

model.add(Activation("relu"))

model.add(Conv2D(filters=16, kernel_size=3, name="conv2"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Conv2D(filters=32, kernel_size=3, name="conv3"))

model.add(Activation("relu"))

model.add(Conv2D(filters=32, kernel_size=3, name="conv4"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Flatten())

model.add(Dense(128))

model.add(Activation("relu"))

model.add(Dense(num_classes, name="features"))

model.add(Activation("softmax"))



# Print the DNN layers

model.summary()



# Train the DNN

lr = 1e-3

optimizer = Adam(lr=lr)

model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])

model.fit(x_train, y_train, verbose=1, 

batch_size=batch_size, epochs=epochs, 

validation_data=(x_test, y_test), 

callbacks=[tb])

And this is what i see in TensorBoard.
(I minimized the Kernels of my first conv layer)
TB Screenshot

What am i missing to visulize all my kernels?

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

add a comment |

Here is my sample code for the Keras model.

tb = TensorBoard(

    log_dir=log_dir, 

    histogram_freq=epochs, 

    write_images=True)



# Define the DNN

model = Sequential()

model.add(Conv2D(filters=16, kernel_size=3, input_shape=(width, height, depth), name="conv1"))

model.add(Activation("relu"))

model.add(Conv2D(filters=16, kernel_size=3, name="conv2"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Conv2D(filters=32, kernel_size=3, name="conv3"))

model.add(Activation("relu"))

model.add(Conv2D(filters=32, kernel_size=3, name="conv4"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Flatten())

model.add(Dense(128))

model.add(Activation("relu"))

model.add(Dense(num_classes, name="features"))

model.add(Activation("softmax"))



# Print the DNN layers

model.summary()



# Train the DNN

lr = 1e-3

optimizer = Adam(lr=lr)

model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])

model.fit(x_train, y_train, verbose=1, 

batch_size=batch_size, epochs=epochs, 

validation_data=(x_test, y_test), 

callbacks=[tb])

And this is what i see in TensorBoard.
(I minimized the Kernels of my first conv layer)
TB Screenshot

What am i missing to visulize all my kernels?

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

add a comment |

Here is my sample code for the Keras model.

tb = TensorBoard(

    log_dir=log_dir, 

    histogram_freq=epochs, 

    write_images=True)



# Define the DNN

model = Sequential()

model.add(Conv2D(filters=16, kernel_size=3, input_shape=(width, height, depth), name="conv1"))

model.add(Activation("relu"))

model.add(Conv2D(filters=16, kernel_size=3, name="conv2"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Conv2D(filters=32, kernel_size=3, name="conv3"))

model.add(Activation("relu"))

model.add(Conv2D(filters=32, kernel_size=3, name="conv4"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Flatten())

model.add(Dense(128))

model.add(Activation("relu"))

model.add(Dense(num_classes, name="features"))

model.add(Activation("softmax"))



# Print the DNN layers

model.summary()



# Train the DNN

lr = 1e-3

optimizer = Adam(lr=lr)

model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])

model.fit(x_train, y_train, verbose=1, 

batch_size=batch_size, epochs=epochs, 

validation_data=(x_test, y_test), 

callbacks=[tb])

And this is what i see in TensorBoard.
(I minimized the Kernels of my first conv layer)
TB Screenshot

What am i missing to visulize all my kernels?

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

Here is my sample code for the Keras model.

tb = TensorBoard(

    log_dir=log_dir, 

    histogram_freq=epochs, 

    write_images=True)



# Define the DNN

model = Sequential()

model.add(Conv2D(filters=16, kernel_size=3, input_shape=(width, height, depth), name="conv1"))

model.add(Activation("relu"))

model.add(Conv2D(filters=16, kernel_size=3, name="conv2"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Conv2D(filters=32, kernel_size=3, name="conv3"))

model.add(Activation("relu"))

model.add(Conv2D(filters=32, kernel_size=3, name="conv4"))

model.add(Activation("relu"))

model.add(MaxPool2D())



model.add(Flatten())

model.add(Dense(128))

model.add(Activation("relu"))

model.add(Dense(num_classes, name="features"))

model.add(Activation("softmax"))



# Print the DNN layers

model.summary()



# Train the DNN

lr = 1e-3

optimizer = Adam(lr=lr)

model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])

model.fit(x_train, y_train, verbose=1, 

batch_size=batch_size, epochs=epochs, 

validation_data=(x_test, y_test), 

callbacks=[tb])

And this is what i see in TensorBoard.
(I minimized the Kernels of my first conv layer)
TB Screenshot

What am i missing to visulize all my kernels?

python keras tensorboard

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

edited Nov 17 '18 at 17:06

asked Nov 17 '18 at 14:36

Franneck

asked Nov 17 '18 at 14:36

Franneck

asked Nov 17 '18 at 14:36

Franneck

add a comment |

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