-sharp build model
model = Sequential()
model.add(LSTM(20, return_sequences=True,
input_shape=(max_len, data_dim)))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss="binary_crossentropy",
optimizer="rmsprop",
metrics=["accuracy"])
model.fit(train_x, train_y, batch_size=16, epochs=10)
score = model.evaluate(test_x, test_y, batch_size=16)
print(score) Dimension information of input data:
shape of train_x: (40960,30)
shape of test_x: (103,60,30)
shape of train_y: (409,1)
shape of test_y: (103,1)
Why the error is reported as follows:
F:\Anaconda\envs\tensorflow\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1591 class_weight=class_weight,
1592 check_batch_axis=False,
-> 1593 batch_size=batch_size)
1594 -sharp Prepare validation data.
1595 do_validation = False
F:\Anaconda\envs\tensorflow\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1428 output_shapes,
1429 check_batch_axis=False,
-> 1430 exception_prefix="target")
1431 sample_weights = _standardize_sample_weights(sample_weight,
1432 self._feed_output_names)
F:\Anaconda\envs\tensorflow\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
108 ": expected " + names[i] + " to have " +
109 str(len(shape)) + " dimensions, but got array "
--> 110 "with shape " + str(data_shape))
111 if not check_batch_axis:
112 data_shape = data_shape[1:]
ValueError: Error when checking target: expected dense_8 to have 3 dimensions, but got array with shape (409, 1)