Python keras_applications.imagenet_utils._obtain_input_shape() Examples

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Example #1
Source File: hypertree_model.py    From costar_plan with Apache License 2.0 4 votes vote down vote up 
def classifier_block(input_tensor, include_top=True, top='classification',
                     classes=1, activation='sigmoid',
                     input_shape=None, final_pooling=None, name='', verbose=1):
    """ Performs the final Activation for the classification of a given problem.

    # Arguments

        include_top: Whether to include the fully-connected
            layer at the top of the network. Also maps to require_flatten
            option in `keras.applications.imagenet_utils._obtain_input_shape()`.
    """
    x = input_tensor
    if include_top and top == 'classification':
        if verbose:
            print("    classification of x: " + str(x))
        x = Dense(units=classes, activation=activation,
                  kernel_initializer="he_normal", name=name + 'fc' + str(classes))(x)

    elif include_top and top == 'segmentation':
        if verbose > 0:
            print("    segmentation of x: " + str(x))
        x = Conv2D(classes, (1, 1), activation='linear', padding='same')(x)

        if K.image_data_format() == 'channels_first':
            channel, row, col = input_shape
        else:
            row, col, channel = input_shape

        x = Reshape((row * col, classes))(x)
        x = Activation(activation)(x)
        x = Reshape((row, col, classes))(x)
    elif include_top and top == 'quaternion':
        x = Dense(units=classes, activation='linear',
                  kernel_initializer="he_normal", name=name + 'fc' + str(classes))(x)
        # normalize the output so we have a unit quaternion
        x = Lambda(lambda x: K.l2_normalize(x, axis=1))(x)
    elif final_pooling == 'avg':
        if verbose:
            print("    GlobalAveragePooling2D")
        x = GlobalAveragePooling2D()(x)

    elif final_pooling == 'max':
        if verbose:
            print("    GlobalMaxPooling2D")
        x = GlobalMaxPooling2D()(x)
    else:
        raise ValueError('hypertree_model.py::classifier_block() unsupported top: ' + str(top))
    return x