How to solve "No Algorithm Worked" Keras Error?
Solution 1
add the following to your code:
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
And then restart the python kernel.
Solution 2
Had the same issue.
The padding='same'
for MaxPooling didn't work for me.
I changed the color_mode
parameter in the train and test generators from 'rgb'
to 'grayscale'
and then it worked for me.
Solution 3
This worked for me:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
Solution 4
My problem was that I called the model with an input_shape of (?,28,28,1) and later called it with (?,28,28,3).
Solution 5
In my case, this was solved by ending all processes, that still allocated memory on one of the GPUs. Apparently, one of them did not finish (correctly). I did not have to change any code.
Niloy Chakraborty
A learner| Very Frequent User| ML and IoT enthusiast
Updated on July 22, 2022Comments
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Niloy Chakraborty almost 2 years
I tried to develop an FCN-16 model in Keras. I initialized the weights with similar FCN-16 model weights.
def FCN8 (nClasses, input_height=256, input_width=256): ## input_height and width must be devisible by 32 because maxpooling with filter size = (2,2) is operated 5 times, ## which makes the input_height and width 2^5 = 32 times smaller assert input_height % 32 == 0 assert input_width % 32 == 0 IMAGE_ORDERING = "channels_last" img_input = Input(shape=(input_height, input_width, 3)) ## Assume 224,224,3 ## Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1', data_format=IMAGE_ORDERING)( img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2', data_format=IMAGE_ORDERING)(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x) f1 = x # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1', data_format=IMAGE_ORDERING)(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2', data_format=IMAGE_ORDERING)(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING)(x) f2 = x # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1', data_format=IMAGE_ORDERING)(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2', data_format=IMAGE_ORDERING)(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3', data_format=IMAGE_ORDERING)(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING)(x) pool3 = x # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1', data_format=IMAGE_ORDERING)(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2', data_format=IMAGE_ORDERING)(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3', data_format=IMAGE_ORDERING)(x) pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)( x) ## (None, 14, 14, 512) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1', data_format=IMAGE_ORDERING)(pool4) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2', data_format=IMAGE_ORDERING)(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3', data_format=IMAGE_ORDERING)(x) pool5 = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)( x) n = 4096 o = (Conv2D(n, (7, 7), activation='relu', padding='same', name="fc6", data_format=IMAGE_ORDERING))(pool5) conv7 = (Conv2D(n, (1, 1), activation='relu', padding='same', name="fc7", data_format=IMAGE_ORDERING))(o) conv7 = (Conv2D(nClasses, (1, 1), activation='relu', padding='same', name="conv7_1", data_format=IMAGE_ORDERING))(conv7) conv7_4 = Conv2DTranspose(nClasses, kernel_size=(2, 2), strides=(2, 2), data_format=IMAGE_ORDERING)( conv7) pool411 = ( Conv2D(nClasses, (1, 1), activation='relu', padding='same', name="pool4_11",use_bias=False, data_format=IMAGE_ORDERING))(pool4) o = Add(name="add")([pool411, conv7_4]) o = Conv2DTranspose(nClasses, kernel_size=(16, 16), strides=(16, 16), use_bias=False, data_format=IMAGE_ORDERING)(o) o = (Activation('softmax'))(o) GDI= Model(img_input, o) GDI.load_weights(Model_Weights_path) model = Model(img_input, o) return model
Then I did train, test split and trying to run the model as:
from keras import optimizers sgd = optimizers.SGD(lr=1E-2, momentum=0.91,decay=5**(-4), nesterov=True) model.compile(optimizer='sgd',loss='categorical_crossentropy',metrics=['accuracy'],) hist1 = model.fit(X_train,y_train,validation_data=(X_test,y_test),batch_size=32,epochs=1000,verbose=2) model.save("/content/drive/My Drive/HCI_prep/new.h5")
But this code is throwing error in the first epoch:
NotFoundError: 2 root error(s) found. (0) Not found: No algorithm worked! [[{{node pool4_11_3/Conv2D}}]] [[loss_4/mul/_629]] (1) Not found: No algorithm worked! [[{{node pool4_11_3/Conv2D}}]] 0 successful operations. 0 derived errors ignored.