tf.keras

I am doing the image semantic segmentation job with unet, if I set the Softmax Activation for last layer like this: ... conv9 = Conv2D(n_classes, (3,3), padding = 'same')(conv9) conv10 = (Activation('softmax'))(conv9) model = Model(inputs, conv10) return model ... and then using loss = tf.keras.losses.CategoricalCrossentropy(from_logits=False) The training will not converge even for only one training image. But if I do not set the Softmax Activation for last layer like this: ... conv9 = Conv2D(n_classes, (3,3), padding = 'same')(conv9) model = Model(inputs, conv9) return model ... and then

My question is: Are the tf.nn.dynamic_rnn and keras.layers.RNN(cell) truly identical as stated in docs? I am planning on building an RNN, however, it seems that tf.nn.dynamic_rnn is depricated in favour of Keras. In particular, it states that: Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use keras.layers.RNN(cell), which is equivalent to this API But I don't see how the APIs are equivalent, in the case of variable sequence lengths! In raw TF, we can specify a tensor of shape (batch_size, seq_lengths) . This way, if our sequence

I wish to view the final output of training a tf.keras model. In this case it would be an array of predictions from the softmax function, e.g. [0,0,0,1,0,1]. Other threads on here have suggested using model.predict(training_data), but this won't work for my situation since I am using dropout at training and validation, so neurons are randomly dropped and predicting again with the same data will give a different result. def get_model(): inputs = tf.keras.layers.Input(shape=(input_dims,)) x = tf.keras.layers.Dropout(rate=dropout_rate)(inputs, training=True) x = tf.keras.layers.Dense(units=29,