python - Keras Error when fitting the data into the model -

i have developed keras model , trying fit data model. here code model.fit

def train(run_name, start_epoch, stop_epoch, img_w): # input parameters     img_h = 64     words_per_epoch = 300     val_split = 0.2     val_words = int(words_per_epoch * (val_split))      # network parameters     conv_filters = 16     kernel_size = (3, 3)     pool_size = 2     time_dense_size = 32     rnn_size = 256     minibatch_size = 32      if k.image_data_format() == 'channels_first':         input_shape = (1, img_w, img_h)     else:         input_shape = (img_w, img_h, 1)       act = 'relu'     input_data = input(name='the_input', shape=input_shape, dtype='float32')     inner = conv2d(conv_filters, kernel_size, padding='same',                    activation=act, kernel_initializer='he_normal',                    name='conv1')(input_data)     inner = maxpooling2d(pool_size=(pool_size, pool_size), name='max1')(inner)     inner = conv2d(conv_filters, kernel_size, padding='same',                    activation=act, kernel_initializer='he_normal',                    name='conv2')(inner)     inner = maxpooling2d(pool_size=(pool_size, pool_size), name='max2')(inner)      conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)     inner = reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)      # cuts down input size going rnn:     inner = dense(time_dense_size, activation=act, name='dense1')(inner)      # 2 layers of bidirectional grus     # gru seems work well, if not better lstm:     gru_1 = gru(rnn_size, return_sequences=true, kernel_initializer='he_normal', name='gru1')(inner)     gru_1b = gru(rnn_size, return_sequences=true, go_backwards=true, kernel_initializer='he_normal', name='gru1_b')(inner)     gru1_merged = add([gru_1, gru_1b])     gru_2 = gru(rnn_size, return_sequences=true, kernel_initializer='he_normal', name='gru2')(gru1_merged)     gru_2b = gru(rnn_size, return_sequences=true, go_backwards=true, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)      # transforms rnn output character activations:     # print("output size",img_gen.get_output_size())      inner = dense(47, kernel_initializer='he_normal',                   name='dense2')(concatenate([gru_2, gru_2b]))     y_pred = activation('softmax', name='softmax')(inner)     # model(inputs=input_data, outputs=y_pred).summary()      labels = input(name='the_labels', shape=[10], dtype='float32')     input_length = input(name='input_length', shape=[1], dtype='int64')     label_length = input(name='label_length', shape=[1], dtype='int64')     # keras doesn't support loss funcs parameters     # ctc loss implemented in lambda layer     loss_out = lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])      # clipnorm seems speeds convergence     sgd = sgd(lr=0.02, decay=1e-6, momentum=0.9, nesterov=true, clipnorm=5)      model = model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)      # loss calc occurs elsewhere, use dummy lambda func loss     model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)      test_func = k.function([input_data], [y_pred])      # viz_cb = vizcallback(run_name, test_func, img_gen.next_val())     (x_train, y_train, train_input_length, train_labels_length), (x_test, y_test, test_input_length, test_labels_length) = dataset_load('./ocr_bangladata.pkl.gz')     print(y_train[0])      x_train = x_train.reshape(x_train.shape[0], 128,64,1)     x_test = x_test.reshape(x_test.shape[0], 128,64,1)     x_train = x_train.astype('float32')     x_test = x_test.astype('float32')      model.fit((np.array(x_train), np.array(y_train),np.array(train_input_length), np.array(train_labels_length)), batch_size=32, epochs=120, verbose=1,validation_data=[np.array(x_test), np.array(y_test),np.array(test_input_length), np.array(test_labels_length)]) 

after getting error

typeerror: error when checking model input: data should numpy array, or list/dict of numpy arrays. 

i tried print data types of each array. result

<type 'numpy.ndarray'> 

but still getting error. there specific reason it? using tensorflow model backend of keras.

here have 4 inputs:

model = model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) 

each of 4 inputs must numpy array, , fit method wants them in list (you're using tuple):

model.fit([x_train, y_train,train_input_length, train_labels_length],...) 

and you're missing outputs in fit method. must match defined loss_out when creating model.