I have this error when running training on my model. I found this issue on different sites, but could not find a solution to my problem.
Here is my model :
import keras import tensorflow as tf import tensorflow.keras.layers as L import tensorflow.keras.models as M import tensorflow.keras.callbacks as C import tensorflow.keras.utils as U def make_model_lstm_pooling(inshape=50000): z = L.Input(shape=(inshape, 10)) x = L.AveragePooling1D(pool_size=1, strides=100)(z) x = L.Bidirectional( L.LSTM(10, dropout=0.1, return_sequences=False, kernel_initializer='ones', bias_initializer='zeros') )(x) x = L.Dense(10, activation='linear')(x) x = L.Dense(1, activation='linear')(x) model = tf.keras.Model(z, x) model.compile(optimizer='adam') return model
I run the training then :
callback_lr = C.ReduceLROnPlateau( monitor='val_loss', patience=3, verbose=0, mode='min') checkpoint = C.ModelCheckpoint( filepath='best_pool.h5', save_best_only=True, monitor='val_loss', mode='min') model = make_model_lstm_pooling() model.summary() history = model.fit( X_train, Y_train, validation_data=(X_dev, Y_dev), epochs=100, callbacks=[checkpoint, callback_lr] )
The whole error is this one :
ValueError: No gradients provided for any variable: ['bidirectional_16/forward_lstm_50/lstm_cell_83/kernel:0', 'bidirectional_16/forward_lstm_50/lstm_cell_83/recurrent_kernel:0', 'bidirectional_16/forward_lstm_50/lstm_cell_83/bias:0', 'bidirectional_16/backward_lstm_50/lstm_cell_84/kernel:0', 'bidirectional_16/backward_lstm_50/lstm_cell_84/recurrent_kernel:0', 'bidirectional_16/backward_lstm_50/lstm_cell_84/bias:0', 'dense_90/kernel:0', 'dense_90/bias:0', 'dense_91/kernel:0', 'dense_91/bias:0'].
I get the problem when running
I saw that the problem can appear when using bad types : I have
float type in the input and
int in the labels. I have NO nan in the input.
I see in the error there is a problem related to the kernel initializer, the default is
glorot_uniform, it appears to me it is not zeros, if I am not mistaken.
I tried to change
kernel_initializer but did not improve.
Something else : I made a test on several samples, and in my test I have less samples then features. Have anyone any idea if the problem is related to this ?
Any help will be appreciated.