Passing labels for tiny-yolo model using keras, expected activation_7 to have 4 dimensions, but got array...
0
I am trying to pass my own data from scratch through the tiny-YOLO architecture built using Keras.
In my understanding,
the labels for each image should be of the form:
[p_c, b_x, b_y, b_h, b_w, c_1, c_2] or variants for other cases.
In this case, there are 7 elements in each label for an image.
Final layer is a convolutional layer with output (3, 3, 7), with each channel representing the label.
However, during training and at the final layer, the linear activation of the convolutional layer expects 4 dimensions whilst the labels I've provided is of the form (m, 7) as explained above. (note: m = number of training data).
What should I do to fix this? Any help will be appreciated!
Below is a model summary and the error at the bottom.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 100, 100, 16) 448
_________________________________________________________________
activation_1 (Activation) (None, 100, 100, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 50, 50, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 50, 50, 32) 4640
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 50, 50, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 25, 25, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 25, 25, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 25, 25, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 128) 73856
_________________________________________________________________
activation_3 (Activation) (None, 12, 12, 128) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 6, 6, 512) 590336
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 512) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 512) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 3, 3, 1024) 4719616
_________________________________________________________________
activation_5 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 3, 3, 1024) 9438208
_________________________________________________________________
activation_6 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 3, 3, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 3, 3, 7) 0
=================================================================
Total params: 14,852,775
Trainable params: 14,852,775
Non-trainable params: 0
_________________________________________________________________
Error:
ValueError: Error when checking target: expected activation_7 to have 4 dimensions, but got array with shape (246, 7)
Model Code:
def build_model(img_rows, img_cols, num_channels):
model = Sequential()
input_shape = (img_rows, img_cols, num_channels)
if K.image_data_format() == "channels_first":
input_shape = (num_channels, img_rows, img_cols)
model.add(Conv2D(filters=16, kernel_size=3, padding="same", input_shape=input_shape))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=32, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=64, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=128, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=512, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
#Softmax layer (filters: p_c, b_x, b_y, b_w, b_h, 2 classes)
model.add(Conv2D(filters=7, kernel_size=1, padding="same"))
model.add(Activation("linear"))
return model
Training Code:
def main():
# Put training data and labels into variables.
(train_images, train_labels), (test_images, test_labels) = get_data()
# Normalize training data variables.
train_images = train_images.astype("float32")/225.0
test_images = test_images.astype("float32")/225.0
# Create tiny-Yolo model
model = TinyYolo.build_model(100, 100, 3)
model.summary()
optimizer = SGD(lr = 0.01)
model.compile(loss = "categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
model.fit(train_images, train_labels, batch_size=12, epochs=20, verbose=1)
(loss, accuracy) = model.evaluate(test_images, test_labels, batch_size=12,verbose=1)
python tensorflow keras yolo
asked Nov 19 at 10:31
Jack-P
42
add a comment |
0
I am trying to pass my own data from scratch through the tiny-YOLO architecture built using Keras.
In my understanding,
the labels for each image should be of the form:
[p_c, b_x, b_y, b_h, b_w, c_1, c_2] or variants for other cases.
In this case, there are 7 elements in each label for an image.
Final layer is a convolutional layer with output (3, 3, 7), with each channel representing the label.
However, during training and at the final layer, the linear activation of the convolutional layer expects 4 dimensions whilst the labels I've provided is of the form (m, 7) as explained above. (note: m = number of training data).
What should I do to fix this? Any help will be appreciated!
Below is a model summary and the error at the bottom.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 100, 100, 16) 448
_________________________________________________________________
activation_1 (Activation) (None, 100, 100, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 50, 50, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 50, 50, 32) 4640
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 50, 50, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 25, 25, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 25, 25, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 25, 25, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 128) 73856
_________________________________________________________________
activation_3 (Activation) (None, 12, 12, 128) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 6, 6, 512) 590336
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 512) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 512) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 3, 3, 1024) 4719616
_________________________________________________________________
activation_5 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 3, 3, 1024) 9438208
_________________________________________________________________
activation_6 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 3, 3, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 3, 3, 7) 0
=================================================================
Total params: 14,852,775
Trainable params: 14,852,775
Non-trainable params: 0
_________________________________________________________________
Error:
ValueError: Error when checking target: expected activation_7 to have 4 dimensions, but got array with shape (246, 7)
Model Code:
def build_model(img_rows, img_cols, num_channels):
model = Sequential()
input_shape = (img_rows, img_cols, num_channels)
if K.image_data_format() == "channels_first":
input_shape = (num_channels, img_rows, img_cols)
model.add(Conv2D(filters=16, kernel_size=3, padding="same", input_shape=input_shape))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=32, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=64, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=128, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=512, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
#Softmax layer (filters: p_c, b_x, b_y, b_w, b_h, 2 classes)
model.add(Conv2D(filters=7, kernel_size=1, padding="same"))
model.add(Activation("linear"))
return model
Training Code:
def main():
# Put training data and labels into variables.
(train_images, train_labels), (test_images, test_labels) = get_data()
# Normalize training data variables.
train_images = train_images.astype("float32")/225.0
test_images = test_images.astype("float32")/225.0
# Create tiny-Yolo model
model = TinyYolo.build_model(100, 100, 3)
model.summary()
optimizer = SGD(lr = 0.01)
model.compile(loss = "categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
model.fit(train_images, train_labels, batch_size=12, epochs=20, verbose=1)
(loss, accuracy) = model.evaluate(test_images, test_labels, batch_size=12,verbose=1)
python tensorflow keras yolo
asked Nov 19 at 10:31
Jack-P
42
1
Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39
add a comment |
0
0
0
I am trying to pass my own data from scratch through the tiny-YOLO architecture built using Keras.
In my understanding,
the labels for each image should be of the form:
[p_c, b_x, b_y, b_h, b_w, c_1, c_2] or variants for other cases.
In this case, there are 7 elements in each label for an image.
Final layer is a convolutional layer with output (3, 3, 7), with each channel representing the label.
However, during training and at the final layer, the linear activation of the convolutional layer expects 4 dimensions whilst the labels I've provided is of the form (m, 7) as explained above. (note: m = number of training data).
What should I do to fix this? Any help will be appreciated!
Below is a model summary and the error at the bottom.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 100, 100, 16) 448
_________________________________________________________________
activation_1 (Activation) (None, 100, 100, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 50, 50, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 50, 50, 32) 4640
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 50, 50, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 25, 25, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 25, 25, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 25, 25, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 128) 73856
_________________________________________________________________
activation_3 (Activation) (None, 12, 12, 128) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 6, 6, 512) 590336
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 512) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 512) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 3, 3, 1024) 4719616
_________________________________________________________________
activation_5 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 3, 3, 1024) 9438208
_________________________________________________________________
activation_6 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 3, 3, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 3, 3, 7) 0
=================================================================
Total params: 14,852,775
Trainable params: 14,852,775
Non-trainable params: 0
_________________________________________________________________
Error:
ValueError: Error when checking target: expected activation_7 to have 4 dimensions, but got array with shape (246, 7)
Model Code:
def build_model(img_rows, img_cols, num_channels):
model = Sequential()
input_shape = (img_rows, img_cols, num_channels)
if K.image_data_format() == "channels_first":
input_shape = (num_channels, img_rows, img_cols)
model.add(Conv2D(filters=16, kernel_size=3, padding="same", input_shape=input_shape))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=32, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=64, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=128, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=512, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
#Softmax layer (filters: p_c, b_x, b_y, b_w, b_h, 2 classes)
model.add(Conv2D(filters=7, kernel_size=1, padding="same"))
model.add(Activation("linear"))
return model
Training Code:
def main():
# Put training data and labels into variables.
(train_images, train_labels), (test_images, test_labels) = get_data()
# Normalize training data variables.
train_images = train_images.astype("float32")/225.0
test_images = test_images.astype("float32")/225.0
# Create tiny-Yolo model
model = TinyYolo.build_model(100, 100, 3)
model.summary()
optimizer = SGD(lr = 0.01)
model.compile(loss = "categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
model.fit(train_images, train_labels, batch_size=12, epochs=20, verbose=1)
(loss, accuracy) = model.evaluate(test_images, test_labels, batch_size=12,verbose=1)
python tensorflow keras yolo
asked Nov 19 at 10:31
Jack-P
42
I am trying to pass my own data from scratch through the tiny-YOLO architecture built using Keras.
In my understanding,
the labels for each image should be of the form:
[p_c, b_x, b_y, b_h, b_w, c_1, c_2] or variants for other cases.
In this case, there are 7 elements in each label for an image.
Final layer is a convolutional layer with output (3, 3, 7), with each channel representing the label.
However, during training and at the final layer, the linear activation of the convolutional layer expects 4 dimensions whilst the labels I've provided is of the form (m, 7) as explained above. (note: m = number of training data).
What should I do to fix this? Any help will be appreciated!
Below is a model summary and the error at the bottom.
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 100, 100, 16) 448
_________________________________________________________________
activation_1 (Activation) (None, 100, 100, 16) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 50, 50, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 50, 50, 32) 4640
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 50, 50, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 25, 25, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 25, 25, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 25, 25, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 12, 12, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 128) 73856
_________________________________________________________________
activation_3 (Activation) (None, 12, 12, 128) 0
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 6, 6, 512) 590336
_________________________________________________________________
activation_4 (Activation) (None, 6, 6, 512) 0
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 512) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 3, 3, 1024) 4719616
_________________________________________________________________
activation_5 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 3, 3, 1024) 9438208
_________________________________________________________________
activation_6 (Activation) (None, 3, 3, 1024) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 3, 3, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 3, 3, 7) 0
=================================================================
Total params: 14,852,775
Trainable params: 14,852,775
Non-trainable params: 0
_________________________________________________________________
Error:
ValueError: Error when checking target: expected activation_7 to have 4 dimensions, but got array with shape (246, 7)
Model Code:
def build_model(img_rows, img_cols, num_channels):
model = Sequential()
input_shape = (img_rows, img_cols, num_channels)
if K.image_data_format() == "channels_first":
input_shape = (num_channels, img_rows, img_cols)
model.add(Conv2D(filters=16, kernel_size=3, padding="same", input_shape=input_shape))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=32, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=64, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=128, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=512, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2), strides=2))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
model.add(Conv2D(filters=1024, kernel_size=3, padding="same"))
# model.add(LeakyReLU())
model.add(Activation("relu"))
#Softmax layer (filters: p_c, b_x, b_y, b_w, b_h, 2 classes)
model.add(Conv2D(filters=7, kernel_size=1, padding="same"))
model.add(Activation("linear"))
return model
Training Code:
def main():
# Put training data and labels into variables.
(train_images, train_labels), (test_images, test_labels) = get_data()
# Normalize training data variables.
train_images = train_images.astype("float32")/225.0
test_images = test_images.astype("float32")/225.0
# Create tiny-Yolo model
model = TinyYolo.build_model(100, 100, 3)
model.summary()
optimizer = SGD(lr = 0.01)
model.compile(loss = "categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
model.fit(train_images, train_labels, batch_size=12, epochs=20, verbose=1)
(loss, accuracy) = model.evaluate(test_images, test_labels, batch_size=12,verbose=1)
python tensorflow keras yolo
python tensorflow keras yolo
asked Nov 19 at 10:31
Jack-P
42
asked Nov 19 at 10:31
Jack-P
42
edited Nov 19 at 23:09
asked Nov 19 at 10:31
Jack-P
42
asked Nov 19 at 10:31
Jack-P
42
asked Nov 19 at 10:31
Jack-P
42
42
1
Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39
add a comment |
1
Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39
1
1
Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39
Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39
add a comment |
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Add the code for creating the model. and training.
– Dinari
Nov 19 at 10:39