<jupyter_start><jupyter_text><jupyter_code>X_train = np.asarray(X_train)
y_train = np.asarray(y_train)
X_train = preprocessing.normalize(X_train)
embed_dim = 128
lstm_out = 200
batch_size = 36

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X_train.shape[1], dropout = 0.2))
model.add(LSTM(lstm_out, dropout_U = 0.2, dropout_W = 0.2))
model.add(Dense(2,activation='softmax'))
model.compile(loss = 'sparse_categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())
model.fit(X_train, y_train, nb_epoch=10, batch_size=64)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15016501650165018, 'fraction_numerical': 0.039603960396039604, 'mean_word_length': 8.950819672131148, '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': '242179', 'license': 'MIT', 'n_tokens_mistral': 264, 'n_tokens_neox': 244, 'n_words': 34, 'path': 'model/model.ipynb', 'repo_name': 'llucifer97/Pose-analysis-of-art', 'size': '606'}