Изменения

'''from ''' __future__ '''import ''' print_function '''from ''' keras.preprocessing '''import ''' sequence '''from ''' keras.models '''import ''' Sequential '''from ''' keras.layers '''import ''' Dense, Dropout, Activation '''from ''' keras.layers '''import ''' Embedding '''from ''' keras.layers '''import ''' Conv1D, GlobalMaxPooling1D '''from ''' keras.datasets '''import ''' imdb

<font color="green"># set parameters:</font> max_features = <font color="blue">5000</font> maxlen = <font color="blue">400</font> batch_size = <font color="blue">32</font> embedding_dims = <font color="blue">50</font> filters = <font color="blue">250</font> kernel_size = <font color="blue">3</font> hidden_dims = <font color="blue">250</font> epochs = <font color="blue">2</font>

print(len(x_train), <font color="red">'train sequences'</font>) print(len(x_test), <font color="red">'test sequences'</font>)

print(<font color="red">'Pad sequences (samples x time)'</font>)

print(<font color="red">'x_train shape:'</font>, x_train.shape) print(<font color="red">'x_test shape:'</font>, x_test.shape)

model.add(Dropout(<font color="blue">0.2</font>))

padding=<font color="red">'valid'</font>, activation=<font color="red">'relu'</font>, strides=<font color="blue">1</font>))

model.add(Dropout(<font color="blue">0.2</font>)) model.add(Activation(<font color="red">'relu'</font>)) model.add(Dense(<font color="blue">1</font>)) model.add(Activation(<font color="red">'sigmoid'</font>))

model.compile(loss=<font color="red">'binary_crossentropy'</font>, optimizer=<font color="red">'adam'</font>, metrics=[<font color="red">'accuracy'</font>])