<jupyter_start><jupyter_text>@title Default title textfrom keras import backend as KK.clear_session()tf.compat.v1.reset_default_graph()<jupyter_code>print(model8.layers[-1].output_shape)
y.shape
from keras.layers import Input, Dense
from keras.models import Model
from keras.utils import plot_model

from tensorflow import keras
model8.fit([y[:100]],[y_train[:100]],steps_per_epoch=10,epochs=20,validation_data=([y_e[:200]],[y_eval[:200]]),validation_steps=1)
#x_train1[0].dtype<jupyter_output>Train on 100 samples, validate on 200 samples
Epoch 1/20
10/10 [==============================] - 49s 5s/step - loss: 103.2381 - accuracy: 0.0688 - val_loss: 88.9451 - val_accuracy: 0.0419
Epoch 2/20
10/10 [==============================] - 19s 2s/step - loss: 97.2067 - accuracy: 0.0127 - val_loss: 82.5526 - val_accuracy: 0.0289
Epoch 3/20
10/10 [==============================] - 19s 2s/step - loss: 90.5331 - accuracy: 0.0076 - val_loss: 77.2399 - val_accuracy: 0.0243
Epoch 4/20
10/10 [==============================] - 19s 2s/step - loss: 85.2129 - accuracy: 0.0088 - val_loss: 73.7281 - val_accuracy: 0.0223
Epoch 5/20
10/10 [==============================] - 19s 2s/step - loss: 81.8486 - accuracy: 0.0096 - val_loss: 71.3028 - val_accuracy: 0.0216
Epoch 6/20
10/10 [==============================] - 19s 2s/step - loss: 79.4191 - accuracy: 0.0102 - val_loss: 69.5676 - val_accuracy: 0.0211
Epoch 7/20
10/10 [==============================] - 19s 2s/step - loss: 77.6668 - accuracy: 0.0105 - [...]
{'fraction_non_alphanumeric': 0.2701801200800534, 'fraction_numerical': 0.16210807204803201, 'mean_word_length': 4.7034220532319395, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '301979', 'license': 'MIT', 'n_tokens_mistral': 716, 'n_tokens_neox': 546, 'n_words': 123, 'path': 'main.ipynb', 'repo_name': 'rishabh30/Abstraction_and_Reasoning_Challenge', 'size': '1499'}