<jupyter_start><jupyter_text>the following architecture comes from the keras docs... seems to overfit<jupyter_code>from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D

model = Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=(181, 64)))
model.add(Conv1D(64, 3, activation='relu'))
model.add(MaxPooling1D(3))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.8))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=50, epochs=10,
          validation_data=(x_test, y_test), verbose=2)<jupyter_output>Train on 15830 samples, validate on 6785 samples
Epoch 1/10
 - 13s - loss: 0.6888 - acc: 0.5275 - val_loss: 0.6705 - val_acc: 0.5876
Epoch 2/10
 - 12s - loss: 0.6568 - acc: 0.6089 - val_loss: 0.6462 - val_acc: 0.6318
Epoch 3/10
 - 14s - loss: 0.6470 - acc: 0.6318 - val_loss: 0.6423 - val_acc: 0.6335
Epoch 4/10
 - 13s - loss: 0.6381 - acc: 0.6372 - val_loss: 0.6391 - val_acc: 0.6321
Epoch 5/10
 - 13s - loss: 0.6274 - acc: 0.6483 - val_loss: 0.6343 - val_acc: 0.6370
Epoch 6/10
 - 14s - loss: 0.6152 - acc: 0.6653 - val_loss: 0.6348 - val_acc: 0.6327
Epoch 7/10
 - 12s - loss: 0.5949 - acc: 0.6810 - val_loss: 0.6438 - val_acc: 0.6248
Epoch 8/10
 - 13s - loss: 0.5709 - acc: 0.7025 - val_loss: 0.6346 - val_acc: 0.6323
Epoch 9/10
 - 13s - loss: 0.5453 - acc: 0.7233 - val_loss: 0.6692 - val_acc: 0.6327
Epoch 10/10
 - 12s - loss: 0.5106 - acc: 0.7488 - val_loss: 0.6699 - val_acc: 0.6246
{'fraction_non_alphanumeric': 0.14833615341229556, 'fraction_numerical': 0.16751269035532995, 'mean_word_length': 3.468513853904282, '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': '158468', 'license': 'MIT', 'n_tokens_mistral': 933, 'n_tokens_neox': 739, 'n_words': 177, 'path': 'archive/dev_jay/one_hot_CNN_jay.ipynb', 'repo_name': 'cnyambura/NovoNordisk_Capstone', 'size': '1773'}