Error when checking model input: expected no data, but got: - python
I want to extract a part of the SSD512 architecture into a separate model. The function that returns the required model is below:
def ssd_512_end(image_size,
n_classes,
mode='training',
l2_regularization=0.0005,
min_scale=None,
max_scale=None,
scales=None,
aspect_ratios_global=None,
aspect_ratios_per_layer=[[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5, 3.0, 1.0/3.0],
[1.0, 2.0, 0.5, 3.0, 1.0/3.0],
[1.0, 2.0, 0.5, 3.0, 1.0/3.0],
[1.0, 2.0, 0.5, 3.0, 1.0/3.0],
[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5]],
two_boxes_for_ar1=True,
steps=[8, 16, 32, 64, 128, 256, 512],
offsets=None,
clip_boxes=False,
variances=[0.1, 0.1, 0.2, 0.2],
coords='centroids',
normalize_coords=True,
subtract_mean=[123, 117, 104],
divide_by_stddev=None,
swap_channels=[2, 1, 0],
confidence_thresh=0.01,
iou_threshold=0.45,
top_k=200,
nms_max_output_size=400,
return_predictor_sizes=False):
n_predictor_layers = 7 # The number of predictor conv layers in the network is 7 for the original SSD512
n_classes += 1 # Account for the background class.
l2_reg = l2_regularization # Make the internal name shorter.
img_height, img_width, img_channels = image_size[0], image_size[1], image_size[2]
############################################################################
# Get a few exceptions out of the way.
############################################################################
if aspect_ratios_global is None and aspect_ratios_per_layer is None:
raise ValueError("`aspect_ratios_global` and `aspect_ratios_per_layer` cannot both be None. At least one needs to be specified.")
if aspect_ratios_per_layer:
if len(aspect_ratios_per_layer) != n_predictor_layers:
raise ValueError("It must be either aspect_ratios_per_layer is None or len(aspect_ratios_per_layer) == {}, but len(aspect_ratios_per_layer) == {}.".format(n_predictor_layers, len(aspect_ratios_per_layer)))
if (min_scale is None or max_scale is None) and scales is None:
raise ValueError("Either `min_scale` and `max_scale` or `scales` need to be specified.")
if scales:
if len(scales) != n_predictor_layers+1:
raise ValueError("It must be either scales is None or len(scales) == {}, but len(scales) == {}.".format(n_predictor_layers+1, len(scales)))
else: # If no explicit list of scaling factors was passed, compute the list of scaling factors from `min_scale` and `max_scale`
scales = np.linspace(min_scale, max_scale, n_predictor_layers+1)
if len(variances) != 4:
raise ValueError("4 variance values must be pased, but {} values were received.".format(len(variances)))
variances = np.array(variances)
if np.any(variances <= 0):
raise ValueError("All variances must be >0, but the variances given are {}".format(variances))
if (not (steps is None)) and (len(steps) != n_predictor_layers):
raise ValueError("You must provide at least one step value per predictor layer.")
if (not (offsets is None)) and (len(offsets) != n_predictor_layers):
raise ValueError("You must provide at least one offset value per predictor layer.")
############################################################################
# Compute the anchor box parameters.
############################################################################
# Set the aspect ratios for each predictor layer. These are only needed for the anchor box layers.
if aspect_ratios_per_layer:
aspect_ratios = aspect_ratios_per_layer
else:
aspect_ratios = [aspect_ratios_global] * n_predictor_layers
# Compute the number of boxes to be predicted per cell for each predictor layer.
# We need this so that we know how many channels the predictor layers need to have.
if aspect_ratios_per_layer:
n_boxes = []
for ar in aspect_ratios_per_layer:
if (1 in ar) & two_boxes_for_ar1:
n_boxes.append(len(ar) + 1) # +1 for the second box for aspect ratio 1
else:
n_boxes.append(len(ar))
else: # If only a global aspect ratio list was passed, then the number of boxes is the same for each predictor layer
if (1 in aspect_ratios_global) & two_boxes_for_ar1:
n_boxes = len(aspect_ratios_global) + 1
else:
n_boxes = len(aspect_ratios_global)
n_boxes = [n_boxes] * n_predictor_layers
if steps is None:
steps = [None] * n_predictor_layers
if offsets is None:
offsets = [None] * n_predictor_layers
############################################################################
# Define functions for the Lambda layers below.
############################################################################
def identity_layer(tensor):
return tensor
def input_mean_normalization(tensor):
return tensor - np.array(subtract_mean)
def input_stddev_normalization(tensor):
return tensor / np.array(divide_by_stddev)
def input_channel_swap(tensor):
if len(swap_channels) == 3:
return K.stack([tensor[...,swap_channels[0]], tensor[...,swap_channels[1]], tensor[...,swap_channels[2]]], axis=-1)
elif len(swap_channels) == 4:
return K.stack([tensor[...,swap_channels[0]], tensor[...,swap_channels[1]], tensor[...,swap_channels[2]], tensor[...,swap_channels[3]]], axis=-1)
############################################################################
# Build the network.
############################################################################
x = Input(shape=(img_height, img_width, img_channels))
# The following identity layer is only needed so that the subsequent lambda layers can be optional.
x1 = Lambda(identity_layer, output_shape=(img_height, img_width, img_channels), name='identity_layer')(x)
if not (subtract_mean is None):
x1 = Lambda(input_mean_normalization, output_shape=(img_height, img_width, img_channels), name='input_mean_normalization')(x1)
if not (divide_by_stddev is None):
x1 = Lambda(input_stddev_normalization, output_shape=(img_height, img_width, img_channels), name='input_stddev_normalization')(x1)
if swap_channels:
x1 = Lambda(input_channel_swap, output_shape=(img_height, img_width, img_channels), name='input_channel_swap')(x1)
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv1_1')(x1)
conv1_2 = Conv2D(64, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv1_2')(conv1_1)
pool1 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool1')(conv1_2)
conv2_1 = Conv2D(128, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv2_1')(pool1)
conv2_2 = Conv2D(128, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv2_2')(conv2_1)
pool2 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool2')(conv2_2)
conv3_1 = Conv2D(256, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv3_1')(pool2)
conv3_2 = Conv2D(256, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv3_2')(conv3_1)
conv3_3 = Conv2D(256, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv3_3')(conv3_2)
pool3 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool3')(conv3_3)
conv4_1 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv4_1')(pool3)
conv4_2 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv4_2')(conv4_1)
conv4_3 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv4_3')(conv4_2)
pool4 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='pool4')(conv4_3)
conv5_1 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv5_1')(pool4)
conv5_2 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv5_2')(conv5_1)
conv5_3 = Conv2D(512, (3, 3), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv5_3')(conv5_2)
pool5 = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='pool5')(conv5_3)
fc6 = Conv2D(1024, (3, 3), dilation_rate=(6, 6), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='fc6')(pool5)
fc7 = Conv2D(1024, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='fc7')(fc6)
conv6_1 = Conv2D(256, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv6_1')(fc7)
conv6_1 = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv6_padding')(conv6_1)
conv6_2 = Conv2D(512, (3, 3), strides=(2, 2), activation='relu', padding='valid', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv6_2')(conv6_1)
conv7_1 = Conv2D(128, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv7_1')(conv6_2)
conv7_1 = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv7_padding')(conv7_1)
conv7_2 = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv7_2')(conv7_1)
conv8_1 = Conv2D(128, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv8_1')(conv7_2)
conv8_1 = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv8_padding')(conv8_1)
conv8_2 = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv8_2')(conv8_1)
conv9_1 = Conv2D(128, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv9_1')(conv8_2)
conv9_1 = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv9_padding')(conv9_1)
conv9_2 = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv9_2')(conv9_1)
conv10_1 = Conv2D(128, (1, 1), activation='relu', padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv10_1')(conv9_2)
conv10_1 = ZeroPadding2D(padding=((1, 1), (1, 1)), name='conv10_padding')(conv10_1)
conv10_2 = Conv2D(256, (4, 4), strides=(1, 1), activation='relu', padding='valid', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv10_2')(conv10_1)
conv4_3_input = Input(tensor = conv4_3, name = 'conv4_3_input')
fc7_input = Input(tensor = fc7, name = 'fc7_input')
conv6_2_input = Input(tensor = conv6_2, name = 'conv6_2_input')
conv7_2_input = Input(tensor = conv7_2, name = 'conv7_2_input')
conv8_2_input = Input(tensor = conv8_2, name = 'conv8_2_input')
conv9_2_input = Input(tensor = conv9_2, name = 'conv9_2_input')
conv10_2_input = Input(tensor = conv10_2, name = 'conv10_2_input')
# Feed conv4_3 into the L2 normalization layer
conv4_3_norm = L2Normalization(gamma_init=20, name='conv4_3_norm')(conv4_3)
### Build the convolutional predictor layers on top of the base network
# We precidt `n_classes` confidence values for each box, hence the confidence predictors have depth `n_boxes * n_classes`
# Output shape of the confidence layers: `(batch, height, width, n_boxes * n_classes)`
conv4_3_norm_mbox_conf = Conv2D(n_boxes[0] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv4_3_norm_mbox_conf')(conv4_3_norm)
fc7_mbox_conf = Conv2D(n_boxes[1] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='fc7_mbox_conf')(fc7)
conv6_2_mbox_conf = Conv2D(n_boxes[2] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv6_2_mbox_conf')(conv6_2)
conv7_2_mbox_conf = Conv2D(n_boxes[3] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv7_2_mbox_conf')(conv7_2)
conv8_2_mbox_conf = Conv2D(n_boxes[4] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv8_2_mbox_conf')(conv8_2)
conv9_2_mbox_conf = Conv2D(n_boxes[5] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv9_2_mbox_conf')(conv9_2)
conv10_2_mbox_conf = Conv2D(n_boxes[6] * n_classes, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv10_2_mbox_conf')(conv10_2)
# We predict 4 box coordinates for each box, hence the localization predictors have depth `n_boxes * 4`
# Output shape of the localization layers: `(batch, height, width, n_boxes * 4)`
conv4_3_norm_mbox_loc = Conv2D(n_boxes[0] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv4_3_norm_mbox_loc')(conv4_3_norm)
fc7_mbox_loc = Conv2D(n_boxes[1] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='fc7_mbox_loc')(fc7)
conv6_2_mbox_loc = Conv2D(n_boxes[2] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv6_2_mbox_loc')(conv6_2)
conv7_2_mbox_loc = Conv2D(n_boxes[3] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv7_2_mbox_loc')(conv7_2)
conv8_2_mbox_loc = Conv2D(n_boxes[4] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv8_2_mbox_loc')(conv8_2)
conv9_2_mbox_loc = Conv2D(n_boxes[5] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv9_2_mbox_loc')(conv9_2)
conv10_2_mbox_loc = Conv2D(n_boxes[6] * 4, (3, 3), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(l2_reg), name='conv10_2_mbox_loc')(conv10_2)
### Generate the anchor boxes (called "priors" in the original Caffe/C++ implementation, so I'll keep their layer names)
# Output shape of anchors: `(batch, height, width, n_boxes, 8)`
conv4_3_norm_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[0], next_scale=scales[1], aspect_ratios=aspect_ratios[0],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[0], this_offsets=offsets[0], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv4_3_norm_mbox_priorbox')(conv4_3_norm_mbox_loc)
fc7_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[1], next_scale=scales[2], aspect_ratios=aspect_ratios[1],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[1], this_offsets=offsets[1], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='fc7_mbox_priorbox')(fc7_mbox_loc)
conv6_2_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[2], next_scale=scales[3], aspect_ratios=aspect_ratios[2],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[2], this_offsets=offsets[2], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv6_2_mbox_priorbox')(conv6_2_mbox_loc)
conv7_2_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[3], next_scale=scales[4], aspect_ratios=aspect_ratios[3],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[3], this_offsets=offsets[3], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv7_2_mbox_priorbox')(conv7_2_mbox_loc)
conv8_2_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[4], next_scale=scales[5], aspect_ratios=aspect_ratios[4],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[4], this_offsets=offsets[4], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv8_2_mbox_priorbox')(conv8_2_mbox_loc)
conv9_2_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[5], next_scale=scales[6], aspect_ratios=aspect_ratios[5],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[5], this_offsets=offsets[5], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv9_2_mbox_priorbox')(conv9_2_mbox_loc)
conv10_2_mbox_priorbox = AnchorBoxes(img_height, img_width, this_scale=scales[6], next_scale=scales[7], aspect_ratios=aspect_ratios[6],
two_boxes_for_ar1=two_boxes_for_ar1, this_steps=steps[6], this_offsets=offsets[6], clip_boxes=clip_boxes,
variances=variances, coords=coords, normalize_coords=normalize_coords, name='conv10_2_mbox_priorbox')(conv10_2_mbox_loc)
### Reshape
# Reshape the class predictions, yielding 3D tensors of shape `(batch, height * width * n_boxes, n_classes)`
# We want the classes isolated in the last axis to perform softmax on them
conv4_3_norm_mbox_conf_reshape = Reshape((-1, n_classes), name='conv4_3_norm_mbox_conf_reshape')(conv4_3_norm_mbox_conf)
fc7_mbox_conf_reshape = Reshape((-1, n_classes), name='fc7_mbox_conf_reshape')(fc7_mbox_conf)
conv6_2_mbox_conf_reshape = Reshape((-1, n_classes), name='conv6_2_mbox_conf_reshape')(conv6_2_mbox_conf)
conv7_2_mbox_conf_reshape = Reshape((-1, n_classes), name='conv7_2_mbox_conf_reshape')(conv7_2_mbox_conf)
conv8_2_mbox_conf_reshape = Reshape((-1, n_classes), name='conv8_2_mbox_conf_reshape')(conv8_2_mbox_conf)
conv9_2_mbox_conf_reshape = Reshape((-1, n_classes), name='conv9_2_mbox_conf_reshape')(conv9_2_mbox_conf)
conv10_2_mbox_conf_reshape = Reshape((-1, n_classes), name='conv10_2_mbox_conf_reshape')(conv10_2_mbox_conf)
# Reshape the box predictions, yielding 3D tensors of shape `(batch, height * width * n_boxes, 4)`
# We want the four box coordinates isolated in the last axis to compute the smooth L1 loss
conv4_3_norm_mbox_loc_reshape = Reshape((-1, 4), name='conv4_3_norm_mbox_loc_reshape')(conv4_3_norm_mbox_loc)
fc7_mbox_loc_reshape = Reshape((-1, 4), name='fc7_mbox_loc_reshape')(fc7_mbox_loc)
conv6_2_mbox_loc_reshape = Reshape((-1, 4), name='conv6_2_mbox_loc_reshape')(conv6_2_mbox_loc)
conv7_2_mbox_loc_reshape = Reshape((-1, 4), name='conv7_2_mbox_loc_reshape')(conv7_2_mbox_loc)
conv8_2_mbox_loc_reshape = Reshape((-1, 4), name='conv8_2_mbox_loc_reshape')(conv8_2_mbox_loc)
conv9_2_mbox_loc_reshape = Reshape((-1, 4), name='conv9_2_mbox_loc_reshape')(conv9_2_mbox_loc)
conv10_2_mbox_loc_reshape = Reshape((-1, 4), name='conv10_2_mbox_loc_reshape')(conv10_2_mbox_loc)
# Reshape the anchor box tensors, yielding 3D tensors of shape `(batch, height * width * n_boxes, 8)`
conv4_3_norm_mbox_priorbox_reshape = Reshape((-1, 8), name='conv4_3_norm_mbox_priorbox_reshape')(conv4_3_norm_mbox_priorbox)
fc7_mbox_priorbox_reshape = Reshape((-1, 8), name='fc7_mbox_priorbox_reshape')(fc7_mbox_priorbox)
conv6_2_mbox_priorbox_reshape = Reshape((-1, 8), name='conv6_2_mbox_priorbox_reshape')(conv6_2_mbox_priorbox)
conv7_2_mbox_priorbox_reshape = Reshape((-1, 8), name='conv7_2_mbox_priorbox_reshape')(conv7_2_mbox_priorbox)
conv8_2_mbox_priorbox_reshape = Reshape((-1, 8), name='conv8_2_mbox_priorbox_reshape')(conv8_2_mbox_priorbox)
conv9_2_mbox_priorbox_reshape = Reshape((-1, 8), name='conv9_2_mbox_priorbox_reshape')(conv9_2_mbox_priorbox)
conv10_2_mbox_priorbox_reshape = Reshape((-1, 8), name='conv10_2_mbox_priorbox_reshape')(conv10_2_mbox_priorbox)
### Concatenate the predictions from the different layers
# Axis 0 (batch) and axis 2 (n_classes or 4, respectively) are identical for all layer predictions,
# so we want to concatenate along axis 1, the number of boxes per layer
# Output shape of `mbox_conf`: (batch, n_boxes_total, n_classes)
mbox_conf = Concatenate(axis=1, name='mbox_conf')([conv4_3_norm_mbox_conf_reshape,
fc7_mbox_conf_reshape,
conv6_2_mbox_conf_reshape,
conv7_2_mbox_conf_reshape,
conv8_2_mbox_conf_reshape,
conv9_2_mbox_conf_reshape,
conv10_2_mbox_conf_reshape])
# Output shape of `mbox_loc`: (batch, n_boxes_total, 4)
mbox_loc = Concatenate(axis=1, name='mbox_loc')([conv4_3_norm_mbox_loc_reshape,
fc7_mbox_loc_reshape,
conv6_2_mbox_loc_reshape,
conv7_2_mbox_loc_reshape,
conv8_2_mbox_loc_reshape,
conv9_2_mbox_loc_reshape,
conv10_2_mbox_loc_reshape])
# Output shape of `mbox_priorbox`: (batch, n_boxes_total, 8)
mbox_priorbox = Concatenate(axis=1, name='mbox_priorbox')([conv4_3_norm_mbox_priorbox_reshape,
fc7_mbox_priorbox_reshape,
conv6_2_mbox_priorbox_reshape,
conv7_2_mbox_priorbox_reshape,
conv8_2_mbox_priorbox_reshape,
conv9_2_mbox_priorbox_reshape,
conv10_2_mbox_priorbox_reshape])
# The box coordinate predictions will go into the loss function just the way they are,
# but for the class predictions, we'll apply a softmax activation layer first
mbox_conf_softmax = Activation('softmax', name='mbox_conf_softmax')(mbox_conf)
# Concatenate the class and box predictions and the anchors to one large predictions vector
# Output shape of `predictions`: (batch, n_boxes_total, n_classes + 4 + 8)
predictions = Concatenate(axis=2, name='predictions')([mbox_conf_softmax, mbox_loc, mbox_priorbox])
end_model = Model(inputs = [conv4_3_input,fc7_input,conv6_2_input,conv7_2_input,conv8_2_input,conv9_2_input,conv10_2_input], outputs = predictions)
return end_model
So when I try to train the model, it gives me the following error:
ValueError Traceback (most recent call last)
<ipython-input-11-467c36610dec> in <module>()
85 validation_data=val_gen_end,
86 validation_steps=ceil(val_dataset_size/batch_size),
---> 87 initial_epoch=initial_epoch)
88
89 # history = model.fit_generator(generator=train_generator,
c:\python36\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
85 warnings.warn('Update your `' + object_name +
86 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87 return func(*args, **kwargs)
88 wrapper._original_function = func
89 return wrapper
c:\python36\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
2040 outs = self.train_on_batch(x, y,
2041 sample_weight=sample_weight,
-> 2042 class_weight=class_weight)
2043
2044 if not isinstance(outs, list):
c:\python36\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1754 sample_weight=sample_weight,
1755 class_weight=class_weight,
-> 1756 check_batch_axis=True)
1757 if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
1758 ins = x + y + sample_weights + [1.]
c:\python36\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1376 self._feed_input_shapes,
1377 check_batch_axis=False,
-> 1378 exception_prefix='input')
1379 y = _standardize_input_data(y, self._feed_output_names,
1380 output_shapes,
c:\python36\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
56 raise ValueError('Error when checking model ' +
57 exception_prefix + ': '
---> 58 'expected no data, but got:', data)
59 return []
60 if data is None:
ValueError: ('Error when checking model input: expected no data, but got:', [array([[[[1.62046947e+01, 0.00000000e+00, 0.00000000e+00, ...
Since 'end_model' has 7 inputs, I am passing a generator which will yield 7 input data appropriately.
Where am I going wrong? Please help!
Thank you.
You don't give x = Input(shape=(img_height, img_width, img_channels)) as input to Model that is why technically your model has no inputs. The other inputs you have are computed from x as you specify the tensor argument for example in Input(tensor = conv4_3, name = 'conv4_3_input') where conv4_3 is computed from x.
So you need specify the original input when creating the model:
end_model = Model(x, predictions)
Related
Predict Image loose saturation compare to the original after training
I am trying super-resolution of RGB images. I have an input image and output image I run the model and when I predict the output the image loses saturation compared to the original image. The image size is 512x512 pixels. I don't know if this happening because of the conv2dtranspose layer. ImagedataGenerator tf.config.run_functions_eagerly(True) import os import numpy as np import cv2 def generator(idir,odir,batch_size,shuffle ): i_list=os.listdir(idir) o_list=os.listdir(odir) batch_index=0 batch_size = batch_size sample_count=len(i_list) while True: input_image_batch=[] output_image_batch=[] i_list=os.listdir(idir) o_list=os.listdir(odir) batch_size = batch_size sample_count=len(i_list) for i in range(batch_index * batch_size, (batch_index + 1) * batch_size ): #iterate for a batch j=i % sample_count # cycle j value over range of available images k=j % batch_size # cycle k value over batch size if shuffle == True: # if shuffle select a random integer between 0 and sample_count-1 to pick as the image=label pair m=np.random.randint(low=0, high=sample_count-1, size=None, dtype=int) else: m=j path_to_in_img=os.path.join(idir,i_list[m]) path_to_out_img=os.path.join(odir,i_list[m]) input_image=cv2.imread(path_to_in_img) input_image=cv2.resize(input_image,(512,512)) input_image = cv2.cvtColor(input_image,cv2.COLOR_BGR2RGB)#create the target image from the input image output_image=cv2.imread(path_to_out_img) output_image=cv2.resize(output_image,(512,512)) output_image = cv2.cvtColor(output_image,cv2.COLOR_BGR2RGB) input_image_batch.append(input_image) output_image_batch.append(output_image) input_image_array=np.array(input_image_batch) input_image_array = input_image_array / 255.0 output_image_array=np.array(output_image_batch) output_image_array = output_image_array / 255.0 batch_index= batch_index + 1 yield (input_image_array, output_image_array) if batch_index * batch_size > sample_count: batch_index=0 Model from keras.models import Model from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda from keras.metrics import MeanIoU kernel_initializer = 'he_uniform' # also try 'he_normal' but model not converging... ################################################################ def simple_unet_model(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS): #Build the model inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) #s = Lambda(lambda x: x / 255)(inputs) #No need for this if we normalize our inputs beforehand s = inputs #Contraction path c1 = Conv2D(16, (3, 3), kernel_initializer = kernel_initializer,activation='relu',padding='same')(s) c1 = Dropout(0.5)(c1) p1 = MaxPooling2D((2, 2))(c1) c2 = Conv2D(32, (3, 3), kernel_initializer = kernel_initializer,activation='relu',padding='same')(p1) c2 = Dropout(0.5)(c2) p2 = MaxPooling2D((2, 2))(c2) c3 = Conv2D(64, (3, 3), kernel_initializer = kernel_initializer,activation='relu', padding='same')(p2) c3 = Dropout(0.5)(c3) p3 = MaxPooling2D((2, 2))(c3) c4 = Conv2D(128, (3, 3), kernel_initializer = kernel_initializer, activation='relu', padding='same')(p3) c4 = Dropout(0.5)(c4) p4 = MaxPooling2D(pool_size=(2, 2))(c4) c5 = Conv2D(256, (3, 3), kernel_initializer = kernel_initializer,activation='relu', padding='same')(p4) c5 = Dropout(0.5)(c5) #Expansive path u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5) u6 = concatenate([u6, c4]) c6 = Conv2D(128, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u6) c6 = Dropout(0.5)(c6) u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6) u7 = concatenate([u7, c3]) c7 = Conv2D(64, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u7) c7 = Dropout(0.5)(c7) u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7) u8 = concatenate([u8, c2]) c8 = Conv2D(32, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u8) c8 = Dropout(0.5)(c8) u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8) u9 = concatenate([u9, c1], axis=3) c9 = Conv2D(16, (3, 3),kernel_initializer = kernel_initializer,activation='relu', padding='same')(u9) c9 = Dropout(0.5)(c9) outputs = Conv2D(3, (1, 1), activation='sigmoid')(c9) model = Model(inputs, outputs) model.summary() return model model.compile(optimizer='adam',loss='mean_squared_error',metrics=[psnr,ssim,tf.losses.mean_squared_error])
while creating VGGSEGNET giving following error
while implementing VGGSEGNET giving me following error
Below is VGGSEGNET code
Function call is like- model=VGGSegnet(n_classes=1, input_height=224, input_width=224)
On following both versions getting same error
tf version=2.4.1 & 2.2.0
keras version=2.4.3 & 2.4.3
python=3.7 and python 3.8
def VGGSegnet(n_classes, input_height, input_width, vgg_level=3, pretrained_weights = None):
img_input = Input(shape=(input_height, input_width,3 ))
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format='channels_last')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format='channels_last')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool1', data_format='channels_last')(x)
f1 = x
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format='channels_last')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format='channels_last')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format='channels_last')(x)
f2 = x
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format='channels_last')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format='channels_last')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format='channels_last')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool1', data_format='channels_last')(x)
f3 = x
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format='channels_last')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', data_format='channels_last')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', data_format='channels_last')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool1', data_format='channels_last')(x)
f4 = x
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format='channels_last')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', data_format='channels_last')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format='channels_last')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool1', data_format='channels_last')(x)
f5 = x
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dense(1000, activation='softmax', name='predictions')(x)
vgg = Model(img_input, x)
vgg.load_weights("image-segmentation-keras-py3-master/Models/vgg16_weights_th_dim_ordering_th_kernels.hdf5")
levels = [f1, f2, f3, f4, f5]
o = levels[vgg_level]
o = ZeroPadding2D((1,1),data_format='channels_last')(o)
o = Conv2D(512,(3,3),padding='valid',data_format='channels_last')(o)
o = BatchNormalization()(o)
o = UpSampling2D((2,2),data_format='channels_last')(o)
o = ZeroPadding2D((1,1),data_format='channels_last')(o)
o = Conv2D(256,(3,3),padding='valid',data_format='channels_last')(o)
o = BatchNormalization()(o)
o = UpSampling2D((2,2),data_format='channels_last')(o)
o = ZeroPadding2D((1,1),data_format='channels_last')(o)
o = Conv2D(128,(3,3),padding='valid',data_format='channels_last')(o)
o = BatchNormalization()(o)
o = UpSampling2D((2, 2), data_format='channels_last')(o)
o = ZeroPadding2D((1, 1), data_format='channels_last')(o)
o = Conv2D(64, (3, 3), padding='valid', data_format='channels_last')(o)
o = BatchNormalization()(o)
o = UpSampling2D((2, 2), data_format='channels_last')(o)
o = ZeroPadding2D((1, 1), data_format='channels_last')(o)
o = Conv2D(32, (3, 3), padding='valid', data_format='channels_last')(o)
o = BatchNormalization()(o)
#o = UpSampling2D((2, 2), data_format='channels_last')(o)
#o = ZeroPadding2D((1, 1), data_format='channels_last')(o)
o = Conv2D(n_classes,(3,3),padding='same',data_format='channels_last')(o)
#o = BatchNormalization()(o)
o_shape = Model(img_input,o).output_shape
#outputHeight = o_shape[2]
#outputWidth = o_shape[3]
outputHeight = o_shape[2]
outputWidth = o_shape[1]
#o = (Reshape((outputHeight*outputWidth, -1)))(o)
#o = (Permute((1,2)))(o)
o = (Activation('sigmoid'))(o)
model = Model(img_input,o)
model.outputWidth = outputWidth
model.outputHeight = outputHeight
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
error block
Traceback (most recent call last):
File "/scratch/pkasar.dbatu/training/VGGSEGNET_224_224_working_on_20_03_21_on_augmented_images_of_size_256_by_256.py", line 248, in <module>
model=VGGSegnet(n_classes=1, input_height=224, input_width=224)
File "/scratch/pkasar.dbatu/training/VGGSEGNET_224_224_working_on_20_03_21_on_augmented_images_of_size_256_by_256.py", line 56, in VGGSegnet
vgg.load_weights("image-segmentation-keras-py3-master/Models/vgg16_weights_th_dim_ordering_th_kernels.hdf5")
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 2234, in load_weights
hdf5_format.load_weights_from_hdf5_group(f, self.layers)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 710, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/keras/backend.py", line 3706, in batch_set_value
x.assign(np.asarray(value, dtype=dtype(x)))
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/values.py", line 781, in assign
return values_util.on_write_assign(
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/values_util.py", line 140, in on_write_assign
return var._update( # pylint: disable=protected-access
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/values.py", line 940, in _update
return self._update_cross_replica(update_fn, value, **kwargs)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/values.py", line 893, in _update_cross_replica
return self.distribute_strategy.extended.update(
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py", line 2494, in update
return self._update(var, fn, args, kwargs, group)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_strategy.py", line 710, in _update
fn(v, *distribute_utils.select_replica_mirrored(i, args),
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 572, in wrapper
return func(*args, **kwargs)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/distribute/values_util.py", line 139, in <lambda>
assign_fn = lambda var, *a, **kw: var.assign(*a, **kw)
File "/home/pkasar.dbatu/.conda/envs/dl_new/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 888, in assign
raise ValueError(
ValueError: Cannot assign to variable block1_conv1/kernel:0 due to variable shape (3, 3, 3, 64) and value shape (3, 3, 64, 3) are incompatible
I am doing segmentation task using iou as performance metric.
Help me out.
Thank you in advance
How to reshape data to a 2D CNN input?
everyone! I've a problem with data reshaping to fed my 2D CNN. My model is cnn_input = Input(shape=(1, 800, 26), name='cnn_input') cnn_conv1 = Conv2D(filters=16, kernel_size=2, strides=(1, 1), padding='same')(cnn_input) cnn_batch_norm1 = BatchNormalization()(cnn_conv1) cnn_relu1 = ReLU()(cnn_batch_norm1) cnn_pool1 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format='channels_first')(cnn_relu1) cnn_drop1 = Dropout(0.2)(cnn_pool1) cnn_conv2 = Conv2D(filters=32, kernel_size=2, strides=(1, 1), padding='same')(cnn_drop1) cnn_batch_norm2 = BatchNormalization()(cnn_conv2) cnn_relu2 = ReLU()(cnn_batch_norm2) cnn_pool2 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format='channels_first')(cnn_relu2) cnn_drop2 = Dropout(0.2)(cnn_pool2) cnn_conv3 = Conv2D(filters=64, kernel_size=2, strides=(1, 1), padding='same')(cnn_drop2) cnn_batch_norm3 = BatchNormalization()(cnn_conv3) cnn_relu3 = ReLU()(cnn_batch_norm3) cnn_pool3 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format='channels_first')(cnn_relu3) cnn_drop3 = Dropout(0.2)(cnn_pool3) cnn_conv4 = Conv2D(filters=128, kernel_size=2, strides=(1, 1), padding='same')(cnn_drop3) cnn_batch_norm4 = BatchNormalization()(cnn_conv4) cnn_relu4 = ReLU()(cnn_batch_norm4) cnn_pool4 = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), data_format='channels_first')(cnn_relu4) cnn_drop4 = Dropout(0.2)(cnn_pool4) cnn_conv5 = Conv2D(filters=64, kernel_size=2, strides=(1, 1), padding='same')(cnn_drop4) cnn_batch_norm5 = BatchNormalization()(cnn_conv5) cnn_relu5 = ReLU()(cnn_batch_norm5) cnn_pool5 = MaxPooling2D(pool_size=(4, 4), strides=(4, 4), data_format='channels_first')(cnn_relu5) cnn_drop5 = Dropout(0.2)(cnn_pool5) cnn_flat = Flatten()(cnn_drop5) cnn_output = Dense(256)(cnn_flat) cnn = Model(inputs=cnn_input, outputs=cnn_output, name='PCNNA') and the shape of my data is print(X_train.shape, X_test.shape, y_train.shape, y_test.shape) >>> (800, 26) (200, 26) (800,) (200,) But before fit the model, I made the following reshape : X_train = np.reshape(X_train, (1, 800, 26)) and when I run fit() I get this error: ValueError: Error when checking input: expected pcnna_input to have 4 dimensions, but got array with shape (1, 800, 26)
AttributeError: can't set attribute,
I have this vgg model. Which I am using to extract an image features. But I am getting an attribute error: I can't seem to understand the error. I am using keras 2.2.4 and tensorflow 1.13.1 with python 3.7.3. I am using anaconda with spyder as IDE.
import numpy as np
import warnings
#tf.compat.v1.reset_default_graph()
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.preprocessing import image
from tensorflow.python.keras.utils import layer_utils , get_source_inputs
from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
from keras_applications.imagenet_utils import _obtain_input_shape
#from keras.engine.topology import get_source_inputs
#from tensorflow.python.keras.utils.layer_utils import get_source_inputs
WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
def VGG16(include_top=True, weights='imagenet',
input_tensor=None, input_shape=None,
pooling=None,
classes=1000):
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as imagenet with `include_top`'
' as true, `classes` should be 1000')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=48,
data_format=K.image_data_format(),
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = Model(inputs, x, name='vgg16')
# load weights
if weights == 'imagenet':
if include_top:
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
)
else:
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'theano':
layer_utils.convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first':
if include_top:
maxpool = model.get_layer(name='block5_pool')
shape = maxpool.output_shape[1:]
dense = model.get_layer(name='fc1')
layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
model.layers.pop() # Get rid of the classification layer
model.outputs = [model.layers[-1].output]
model.layers[-1].outbound_nodes = [ ]
return model```
I am getting the following error on the second last line of the code:
File "/home/natsu/Final_code/vgg16.py", line 200, in VGG16
model.layers[-1].outbound_nodes = []
File "/home/natsu/anaconda3/envs/py-env/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1639, in __setattr__
super(Layer, self).__setattr__(name, value)
AttributeError: can't set attribute```
I was able to execute your code successfully in tensorflow version 1.15 without any modifications. I was able to execute your code successfully in tensorflow version 2.2.0 with minor modifications shown below - Modify, from tensorflow.python.keras.utils import layer_utils , get_source_inputs to from tensorflow.python.keras.utils import layer_utils from tensorflow.python.keras.utils.layer_utils import get_source_inputs Can you please upgrade the tensorflow version and check if the error is fixed. Hope this answers your question. Happy Learning.
How to use Keras functional API in python function, getting error: TypeError: float() argument must be a string or a number, not 'Dimension'
I am trying to write a convolutional neural network in keras that uses an Inception style module, like the example from keras functional API page. To "simplify" things I decided it would be good to put this module in a python function to be reusable with different filter and input sizes. This however generates the error message:
TypeError: float() argument must be a string or a number, not
'Dimension'
How should I write this function, and what's wrong with this one
"""This is the "inception" module."""
def incepm_v1(out_filters, input_shape)->Model:
from keras.layers import Conv2D, MaxPooling2D, Input
input_img = Input(shape=input_shape)
tower_1 = Conv2D(out_filters, (1, 1), padding='same',
activation='relu')(input_img)
tower_1 = Conv2D(out_filters, (3, 3), padding='same',
activation='relu')(tower_1)
tower_2 = Conv2D(out_filters, (1, 1), padding='same',
activation='relu')(input_img)
tower_2 = Conv2D(out_filters, (5, 5), padding='same',
activation='relu')(tower_2)
tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')
(input_img)
tower_3 = Conv2D(out_filters, (1, 1), padding='same',
activation='relu')(tower_3)
output = keras.layers.concatenate([tower_1, tower_2, tower_3],
axis=1)
model = Model(inputs=input_img, outputs=output)
return model
"This is then used in the following model"
def Unetish_model1(image_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)):
image = Input(shape=image_shape)
#First layer 96X96
conv1 = Conv2D(32, (3,3),padding='same', activation =
'relu'(image)
conv1out = Conv2D(16, (1,1),padding = 'same', activation =
'relu')(conv1)
conv1out = MaxPooling2D((2,2), strides = (2,2))(conv1out)
aux1out = Conv2D(16, (1,1), padding = 'same', activation =
'relu')(conv1)
#Second layer 48x48
#print(conv1out.shape)
conv2 = incepm_v1(64, conv1out.shape[1:])(conv1out)
conv2out = Conv2D(32, (1,1), padding = 'same', activation =
'relu')(conv2)
conv2out = MaxPooling2D((2,2), strides = (2,2))(conv2out)
aux2out = Conv2D(32, (1,1), padding = 'same', activation =
'relu')(conv2)
".... removed for sparsity"
model = Model(inputs =image, outputs = output)
model.summary()
return model
IMAGE_SIZE = 96
Unet = Unetish_model1(image_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
--------------------------------------------------------------------
-------
TypeError Traceback (most recent
call last)
<ipython-input-79-9f51199eb354> in <module>()
1 IMAGE_SIZE = 96
----> 2 Unet = Unetish_model1(image_shape=(IMAGE_SIZE, IMAGE_SIZE,
3))
<ipython-input-78-663bab493362> in Unetish_model1(image_shape)
10 #Second layer 48x48
11 #print(conv1out.shape)
---> 12 conv2 = incepm_v1(64, conv1out.shape[1:])(conv1out)
13 conv2out = Conv2D(32, (1,1), padding = 'same',
activation = 'relu')(conv2)
14 conv2out = MaxPooling2D((2,2), strides = (2,2))
(conv2out)
<ipython-input-72-b8563ad454e6> in incepm_v1(out_filters,
input_shape)
4 input_img = Input(shape=input_shape)
5
----> 6 tower_1 = Conv2D(out_filters, (1, 1), padding='same',
activation='relu')(input_img)
7 tower_1 = Conv2D(out_filters, (3, 3), padding='same',
activation='relu')(tower_1)
8
~/anaconda3/envs/dlib/lib/python3.5/site-
packages/keras/engine/topology.py in __call__(self, inputs,
**kwargs)
588
'`layer.build(batch_input_shape)`')
589 if len(input_shapes) == 1:
--> 590 self.build(input_shapes[0])
591 else:
592 self.build(input_shapes)
~/anaconda3/envs/dlib/lib/python3.5/site-
packages/keras/layers/convolutional.py in build(self,
input_shape)
136 name='kernel',
137
regularizer=self.kernel_regularizer,
--> 138
constraint=self.kernel_constraint)
139 if self.use_bias:
140 self.bias = self.add_weight(shape=
(self.filters,),
~/anaconda3/envs/dlib/lib/python3.5/site-
packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' +
object_name +
90 '` call to the Keras 2 API: '
+ signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~/anaconda3/envs/dlib/lib/python3.5/site-
packages/keras/engine/topology.py in add_weight(self, name,
shape, dtype, initializer, regularizer, trainable, constraint)
409 if dtype is None:
410 dtype = K.floatx()
--> 411 weight = K.variable(initializer(shape),
412 dtype=dtype,
413 name=name,
~/anaconda3/envs/dlib/lib/python3.5/site-
packages/keras/initializers.py in __call__(self, shape, dtype)
207 scale /= max(1., fan_out)
208 else:
--> 209 scale /= max(1., float(fan_in + fan_out) / 2)
210 if self.distribution == 'normal':
211 stddev = np.sqrt(scale)
TypeError: float() argument must be a string or a number, not
'Dimension'
I am using keras version 2.1.3 in an anaconda enviroment with tensorflow backended with gpu
layer_name.shape will give you a tensorflow.python.framework.tensor_shape.TensorShape containing tensorflow.python.framework.tensor_shape.Dimension. In this case, you should use K.int_shape(layer_name), which gives you tuple of integers. from keras.layers import Conv2D, MaxPooling2D, Input, Concatenate from keras.models import Model import keras.backend as K """This is the "inception" module.""" def incepm_v1(out_filters, input_shape)->Model: input_img = Input(shape=input_shape) tower_1 = Conv2D(out_filters, (1, 1), padding='same', activation='relu')(input_img) tower_1 = Conv2D(out_filters, (3, 3), padding='same', activation='relu')(tower_1) tower_2 = Conv2D(out_filters, (1, 1), padding='same', activation='relu')(input_img) tower_2 = Conv2D(out_filters, (5, 5), padding='same', activation='relu')(tower_2) tower_3 = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(input_img) tower_3 = Conv2D(out_filters, (1, 1), padding='same', activation='relu')(tower_3) output = Concatenate(axis=1)([tower_1, tower_2, tower_3]) model = Model(inputs=input_img, outputs=output) return model """This is then used in the following model""" def Unetish_model1(image_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)): image = Input(shape=image_shape) #First layer 96X96 conv1 = Conv2D(32, (3,3),padding='same', activation = 'relu')(image) conv1out = Conv2D(16, (1,1),padding = 'same', activation = 'relu')(conv1) conv1out = MaxPooling2D((2,2), strides = (2,2))(conv1out) aux1out = Conv2D(16, (1,1), padding = 'same', activation = 'relu')(conv1) #Second layer 48x48 #conv2 = incepm_v1(64, conv1out.shape[1:])(conv1out) conv2 = incepm_v1(64, K.int_shape(conv1out)[1:])(conv1out) conv2out = Conv2D(32, (1,1), padding = 'same', activation = 'relu')(conv2) conv2out = MaxPooling2D((2,2), strides = (2,2))(conv2out) aux2out = Conv2D(32, (1,1), padding = 'same', activation = 'relu')(conv2) #".... removed for sparsity" model = Model(inputs =image, outputs = output) model.summary() return model IMAGE_SIZE = 96 Unet = Unetish_model1(image_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)) The code above also fixes the usage of Concatenate layer. And note that this piece of code still can be run successfully since some parts of Unetish_model1 is omitted.