<jupyter_start><jupyter_text>Counting multi-task model parameters in paper of : `Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network` the autoencode part isn't included<jupyter_code>from tensorflow.keras.utils import plot_model
from tensorflow.keras import Model, Input
from tensorflow.keras.layers import MaxPool2D, GlobalMaxPool2D, BatchNormalization, Activation
from tensorflow.keras.layers import Conv2D, Concatenate,Flatten, Dense, Dropout


inputs = Input(shape = (1923))
d1 = Dense(2500)(inputs)
d1 = Dense(1250)(d1)
d1 = Dense(600)(d1)
d1 = Dense(300)(d1)
dx1 = Dense(2)(d1)
dx2 = Dense(2)(d1)
dx3 = Dense(2)(d1)
dx4 = Dense(2)(d1)
dx5 = Dense(2)(d1)

model = Model(inputs, [dx1,dx2,dx3,dx4,dx5])

model.count_params()
plot_model(model, show_shapes=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11708482676224612, 'fraction_numerical': 0.06093189964157706, 'mean_word_length': 7.38, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '565257', 'license': 'MIT', 'n_tokens_mistral': 339, 'n_tokens_neox': 316, 'n_words': 74, 'path': 'paper/01_OutoftheBox_Metablism_CYP450/00_count_parameters.ipynb', 'repo_name': 'riversdark/bidd-molmap', 'size': '837'}