Cannot flatten the output of the crfnn_layer

[plapa]

Hey,

I am trying to flatten the output of the crffnn_layer, because I have a binary classification task, instead of a segmentation (i know... ). I am getting the error of the a dimension mistatch:

`
ValueError: Dimensions must be equal, but are 250000 and 94864 for 'dense_1/MatMul' (op: 'MatMul') with input shapes: [1,250000], [94864,512].

`

The input are 64x64x3 images (channels last), which are convoluted and then fed to the model. Currently I am using the network architecture that is provided with the examples, adapted:
`

    channels, height, weight = 3, 500, 500

    # Input
    input_shape = (height, weight, 3)
    img_input = Input(shape=self.input_shape)
    #img_input = Cropping2D((3,3))(img_input)

    # Add plenty of zero padding
    x = ZeroPadding2D(padding=(218, 218))(img_input)

    # VGG-16 convolution block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='valid', name='conv1_1')(x)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)

    # VGG-16 convolution block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='pool2', padding='same')(x)

    # VGG-16 convolution block output3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='pool3', padding='same')(x)
    pool3 = x

    # VGG-16 convolution block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='pool4', padding='same')(x)
    pool4 = x

    # VGG-16 convolution block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='pool5', padding='same')(x)

    # Fully-connected layers converted to convolution layers
    x = Conv2D(4096, (7, 7), activation='relu', padding='valid', name='fc6')(x)
    x = Dropout(0.5)(x)
    x = Conv2D(4096, (1, 1), activation='relu', padding='valid', name='fc7')(x)
    x = Dropout(0.5)(x)
    x = Conv2D(21, (1, 1), padding='valid', name='score-fr')(x)

    # Deconvolution
    score2 = Conv2DTranspose(1, (4, 4), strides=2, name='score2')(x)

    # Skip connections from pool4
    score_pool4 = Conv2D(1, (1, 1), name='score-pool4')(pool4)
    score_pool4c = Cropping2D((5, 5))(score_pool4)
    score_fused = Add()([score2, score_pool4c])
    score4 = Conv2DTranspose(1, (4, 4), strides=2, name='score4', use_bias=False)(score_fused)

    # Skip connections from pool3
    score_pool3 = Conv2D(1, (1, 1), name='score-pool3')(pool3)
    score_pool3c = Cropping2D((9, 9))(score_pool3)
    score_pool3c = ZeroPadding2D(padding=((1,0), (1,0)))(score_pool3c)


    # Fuse things together
    score_final = Add()([score4, score_pool3c])

    # Final up-sampling and cropping
    upsample = Conv2DTranspose(1, (16, 16), strides=8, name='upsample', use_bias=False)(score_final)
    upscore = Cropping2D(((31, 37), (31, 37)))(upsample)

    output = CrfRnnLayer(image_dims=(height, weight),
                        num_classes=1,
                        theta_alpha=160.,
                        theta_beta=3.,
                        theta_gamma=3.,
                        num_iterations=10,
                        name='crfrnn')([upscore, img_input])

    k = Flatten()(output)

    k = Dense(512, activation='relu')(k)
    k = Dropout(.5)(k)
    k = Dense(512, activation='relu')(k)
    predictions = Dense(1, activation='sigmoid')(k)

    # Build the model
    model = Model(img_input, predictions, name='crfrnn_net')

    return model

`

I am stuck, as my intuition would say that it is fine to flatten the probability map and then feed it to a NN.

Any tips?