TypeError: ('Keyword argument not understood:', 'init')

[DungLeMTA]

I use file SigNet-BHSig260.ipynb on Google Colab, but when i ran code:

network definition

base_network = create_base_network_signet(input_shape)

input_a = Input(shape=(input_shape)) input_b = Input(shape=(input_shape))

because we re-use the same instance base_network,

the weights of the network

will be shared across the two branches

processed_a = base_network(input_a) processed_b = base_network(input_b)

Compute the Euclidean distance between the two vectors in the latent space

distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])

model = Model(input=[input_a, input_b], output=distance)

==>

TypeError Traceback (most recent call last)

in () 1 # network definition ----> 2 base_network = create_base_network_signet(input_shape) 3 4 input_a = Input(shape=(input_shape)) 5 input_b = Input(shape=(input_shape)) 5 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message) 806 for kwarg in kwargs: 807 if kwarg not in allowed_kwargs: --> 808 raise TypeError(error_message, kwarg) 809 810 TypeError: ('Keyword argument not understood:', 'init') ############################################################################### **And this is ''create_base_network_signet'' fuction:** def create_base_network_signet(input_shape): '''Base Siamese Network''' seq = Sequential() seq.add(Conv2D(96, kernel_size=(11, 11), activation='relu', name='conv1_1', strides=4, input_shape= input_shape, init='glorot_uniform', dim_ordering='tf')) seq.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) seq.add(MaxPooling2D((3,3), strides=(2, 2))) seq.add(ZeroPadding2D((2, 2), dim_ordering='tf')) seq.add(Conv2D(256, kernel_size=(5, 5), activation='relu', name='conv2_1', strides=1, init='glorot_uniform', dim_ordering='tf')) seq.add(BatchNormalization(epsilon=1e-06, mode=0, axis=1, momentum=0.9)) seq.add(MaxPooling2D((3,3), strides=(2, 2))) seq.add(Dropout(0.3))# added extra seq.add(ZeroPadding2D((1, 1), dim_ordering='tf')) seq.add(Conv2D(384, kernel_size=(3, 3), activation='relu', name='conv3_1', strides=1, init='glorot_uniform', dim_ordering='tf')) seq.add(ZeroPadding2D((1, 1), dim_ordering='tf')) seq.add(Conv2D(256, kernel_size=(3, 3), activation='relu', name='conv3_2', strides=1, init='glorot_uniform', dim_ordering='tf')) seq.add(MaxPooling2D((3,3), strides=(2, 2))) seq.add(Dropout(0.3))# added extra seq.add(Flatten(name='flatten')) seq.add(Dense(1024, W_regularizer=l2(0.0005), activation='relu', init='glorot_uniform')) seq.add(Dropout(0.5)) seq.add(Dense(128, W_regularizer=l2(0.0005), activation='relu', init='glorot_uniform')) # softmax changed to relu return seq ########################################################################### How can I fix this error? Thank you so much!

[ashwini571]

change init to kernel_initializer