CODEWITHSUNDEEP
pythontensorflowmachine-learningkeras
Daniel Vilas-Boas
Daniel Vilas-Boas

Reputation: 896

Error checking input: expected embedding_1 input to have shape but got shape

I have successfully created my Keras sequential model and trained it for a while. Now I am trying to make some predictions but it fails even using the same data as used on the training phase.

I am getting this error: {ValueError}Error when checking input: expected embedding_1_input to have shape (2139,) but got array with shape (1,)

However, when checking the input I am trying to use, it says (2139,). I would like to know if anyone knows what this might be

    df = pd.read_csv('../../data/parsed-data/data.csv')

    df = ModelUtil().remove_entries_based_on_threshold(df, 'Author', 2)

    #show_column_distribution(df, 'Author')

    y = df.pop('Author')

    le = LabelEncoder()
    le.fit(y)
    encoded_Y = le.transform(y)

    tokenizer, padded_sentences, max_sentence_len \
        = PortugueseTextualProcessing().convert_corpus_to_number(df)

    ModelUtil().save_tokenizer(tokenizer)
    vocab_len = len(tokenizer.word_index) + 1

    glove_embedding = PortugueseTextualProcessing().load_vector(tokenizer)

    embedded_matrix = PortugueseTextualProcessing().build_embedding_matrix(glove_embedding, vocab_len, tokenizer)


    cv_scores = []
    kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=7)
    models = []



    nn = NeuralNetwork()
    nn.build_baseline_model(embedded_matrix, max_sentence_len, vocab_len, len(np_utils.to_categorical(encoded_Y)[0]))

    # Separate some validation samples
    val_data, X, Y = ModelUtil().extract_validation_data(padded_sentences, encoded_Y)

    for train_index, test_index in kfold.split(X, Y):
        # convert integers to dummy variables (i.e. one hot encoded)
        dummy_y = np_utils.to_categorical(Y)
        print("TRAIN:", train_index, "TEST:", test_index)
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = dummy_y[train_index], dummy_y[test_index]
        nn.train(X_train, y_train, 100)

        scores = nn.evaluate_model(X_test, y_test)
        cv_scores.append(scores[1] * 100)
        models.append(nn)

    print("%.2f%% (+/- %.2f%%)" % (np.mean(cv_scores), np.std(cv_scores)))
    best_model = models[cv_scores.index(max(cv_scores))]
    best_model.save_model()
    best_model.predict_entries(X[0])

Method that performs the prediction and model creation

    def build_baseline_model(self, emd_matrix, long_sent_size, vocab_len, number_of_classes):
        self.model = Sequential()
        embedding_layer = Embedding(vocab_len, 100, weights=[emd_matrix], input_length=long_sent_size,
                                        trainable=False)
        self.model.add(embedding_layer)
        self.model.add(Dropout(0.2))
        self.model.add(Flatten())

        # softmax performing better than relu
        self.model.add(Dense(number_of_classes, activation='softmax'))

        self.model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
        return self.model    

def predict_entries(self, entry):
        predictions = self.model.predict_classes(entry)
        # show the inputs and predicted outputs
        print("X=%s, Predicted=%s" % (entry, predictions[0]))
        return predictions

X[0].shape evaluates to : (2139,)

Upvotes: 0

Views: 63

Answers (1)

Joao Vitor
Joao Vitor

Reputation: 26

In you case you should apply a reshape so you can get an array with a unique element that contains the sentence.

X_reshape = X[0].reshape(1, 2139)

best_model.predict_entries(X_reshape)

Upvotes: 1

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