Q&A
kerasでseq2seqのサンプルコードを動かしてみたのですが、何故か動かず困っています.
どこをどのように直せば良いかわからないので、ご教授願います
Using TensorFlow backend. WARNING:tensorflow:From /Users/mac/.pyenv/versions/3.6.3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. (100000, 1, 6, 51) (100000, 1, 3, 51) (100000, 1, 3, 51) Traceback (most recent call last): File "sample_seq2seq.py", line 91, in <module> train.fit([X1, X2], y, epochs=1) File "/Users/mac/.pyenv/versions/3.6.3/lib/python3.6/site-packages/keras/engine/training.py", line 1089, in fit batch_size=batch_size) File "/Users/mac/.pyenv/versions/3.6.3/lib/python3.6/site-packages/keras/engine/training.py", line 757, in _standardize_user_data exception_prefix='input') File "/Users/mac/.pyenv/versions/3.6.3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 131, in standardize_input_data 'with shape ' + str(data_shape)) ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (100000, 1, 6, 51)
from random import randint from numpy import array from numpy import argmax from numpy import array_equal from keras.utils import to_categorical from keras.models import Model from keras.layers import Input from keras.layers import LSTM from keras.layers import Dense # generate a sequence of random integers def generate_sequence(length, n_unique): return [randint(1, n_unique-1) for _ in range(length)] # prepare data for the LSTM def get_dataset(n_in, n_out, cardinality, n_samples): X1, X2, y = list(), list(), list() for _ in range(n_samples): # generate source sequence source = generate_sequence(n_in, cardinality) # define padded target sequence target = source[:n_out] target.reverse() # create padded input target sequence target_in = [0] + target[:-1] # encode src_encoded = to_categorical([source], num_classes=cardinality) tar_encoded = to_categorical([target], num_classes=cardinality) tar2_encoded = to_categorical([target_in], num_classes=cardinality) # store X1.append(src_encoded) X2.append(tar2_encoded) y.append(tar_encoded) return array(X1), array(X2), array(y) # returns train, inference_encoder and inference_decoder models def define_models(n_input, n_output, n_units): # define training encoder encoder_inputs = Input(shape=(None, n_input,)) encoder = LSTM(n_units, return_state=True) encoder_outputs, state_h, state_c = encoder(encoder_inputs) encoder_states = [state_h, state_c] # define training decoder decoder_inputs = Input(shape=(None, n_output)) decoder_lstm = LSTM(n_units, return_sequences=True, return_state=True) decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states) decoder_dense = Dense(n_output, activation='softmax') decoder_outputs = decoder_dense(decoder_outputs) model = Model([encoder_inputs, decoder_inputs], decoder_outputs) # define inference encoder encoder_model = Model(encoder_inputs, encoder_states) # define inference decoder decoder_state_input_h = Input(shape=(n_units,)) decoder_state_input_c = Input(shape=(n_units,)) decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs) decoder_states = [state_h, state_c] decoder_outputs = decoder_dense(decoder_outputs) decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states) # return all models return model, encoder_model, decoder_model # generate target given source sequence def predict_sequence(infenc, infdec, source, n_steps, cardinality): # encode state = infenc.predict(source) # start of sequence input target_seq = array([0.0 for _ in range(cardinality)]).reshape(1, 1, cardinality) # collect predictions output = list() for t in range(n_steps): # predict next char yhat, h, c = infdec.predict([target_seq] + state) # store prediction output.append(yhat[0,0,:]) # update state state = [h, c] # update target sequence target_seq = yhat return array(output) # decode a one hot encoded string def one_hot_decode(encoded_seq): return [argmax(vector) for vector in encoded_seq] # configure problem n_features = 50 + 1 n_steps_in = 6 n_steps_out = 3 # define model train, infenc, infdec = define_models(n_features, n_features, 128) train.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc']) # generate training dataset X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 100000) print(X1.shape,X2.shape,y.shape) # train model train.fit([X1, X2], y, epochs=1) # evaluate LSTM total, correct = 100, 0 for _ in range(total): X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1) target = predict_sequence(infenc, infdec, X1, n_steps_out, n_features) if array_equal(one_hot_decode(y[0]), one_hot_decode(target)): correct += 1 print('Accuracy: %.2f%%' % (float(correct)/float(total)*100.0)) # spot check some examples for _ in range(10): X1, X2, y = get_dataset(n_steps_in, n_steps_out, n_features, 1) target = predict_sequence(infenc, infdec, X1, n_steps_out, n_features) print('X=%s y=%s, yhat=%s' % (one_hot_decode(X1[0]), one_hot_decode(y[0]), one_hot_decode(target)))
回答1件
0
ベストアンサー
まずはErrorと記述されている内容に着目してみましょう
ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (100000, 1, 6, 51)
英語で意味がわからないというのであればGoogle翻訳を使います。
基本的にエラーの内容を調べるのに翻訳ツールは日常的に使うようにしたほうが良いです。
一部の言語を除いて日本語でエラーの内容が出てくることは無いので。
翻訳結果↓
ValueError：入力のチェック中にエラーが発生しました：input_1は3次元であることが期待されていますが、形状（100000、1、6、51）の配列を取得しました
これだけを見ると求められる入力値は3次元に対し、4次元で指定しているのが原因のように見受けられます。
この情報を元に原因を調査してみてはいかがでしょうか。
投稿2019/09/13 07:04
総合スコア179
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2019/09/13 07:33