fit ( x_train, y_train, validation_data = ( x_test, y_test ), nb_epoch = 3, batch_size = 64 ) scores = model. create ( prog = 'dot', format = 'svg' )) model. compile ( loss = 'binary_crossentropy', optimizer = 'adam', metrics = ) # ここでモデルを可視化する。 add ( Dense ( 1, activation = 'sigmoid' )) model. add ( Embedding ( top_words, embedding_vector_length, input_length = max_review_length )) model. ![]() pad_sequences ( x_test, maxlen = max_review_length ) embedding_vector_length = 32 model = Sequential () model. pad_sequences ( x_train, maxlen = max_review_length ) x_test = sequence. load_data ( nb_words = top_words ) max_review_length = 500 x_train = sequence. ![]() seed ( 7 ) top_words = 5000 ( x_train, y_train ), ( x_test, y_test ) = imdb. Import numpy as np import pydot from keras.datasets import imdb from keras.models import Sequential from keras.layers import Dense, LSTM from import Embedding from keras.preprocessing import sequence from _util import model_to_dot from IPython.display import SVG np.
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