ValueError with Dense() layer in Keras - keras

Question

I'm trying to train a model that takes in two inputs, concatenates them, and feeds the result into an LSTM. The last layer is a Dense() call, and the targets are binary vectors (with more than one 1). The task is classification.
My input sequences are 50 rows of 23 timesteps with 5625 features (x_train), and my supplementary input (not really a sequence) is 50 one-hot rows of length 23 (total_hours)
The error I'm getting is:
ValueError: Error when checking target: expected dense_1 to have shape (1, 5625)
but got array with shape (5625, 1)
And my code is:
import numpy as np
from keras.layers import LSTM, Dense, Input, Concatenate
from keras.models import Model
#CREATING DUMMY INPUT
hours_input_1 = np.eye(23)
hours_input_2 = np.eye(23)
hours_input_3 = np.pad(np.eye(4), pad_width=((0, 19), (0, 19)), mode='constant')
hours_input_3 = hours_input_3[:4,]
total_hours = np.vstack((hours_input_1, hours_input_2, hours_input_3))
seq_input = np.random.normal(size=(50, 24, 5625))
y_train = np.array([seq_input[i, -1, :] for i in range(50)])
x_train = np.array([seq_input[i, :-1, :] for i in range(50)])
#print 'total_hours', total_hours.shape #(50, 23)
#print 'x_train', x_train.shape #(50, 23, 5625)
#print 'y_train shape', y_train.shape #(50, 5625)
#MODEL DEFINITION
seq_model_in = Input(shape=(1,), batch_shape=(1, 1, 5625))
hours_model_in = Input(shape=(1,), batch_shape=(1, 1, 1))
merged = Concatenate(axis=-1)([seq_model_in, hours_model_in])
#print merged.shape #(1, 1, 5626) = added the 'hour' on as an extra feature
merged_lstm = LSTM(10, batch_input_shape=(1, 1, 5625), return_sequences=False, stateful=True)(merged)
merged_dense = Dense(5625, activation='sigmoid')(merged_lstm)
model = Model(inputs=[seq_model_in, hours_model_in], outputs=merged_dense)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
#TRAINING
for epoch in range(10):
for i in range(50):
y_true = y_train[i,:]
for j in range(23):
input_1 = np.expand_dims(np.expand_dims(x_train[i][j], axis=1), axis=1)
input_1 = np.reshape(input_1, (1, 1, x_train.shape[2]))
input_2 = np.expand_dims(np.expand_dims(np.array([total_hours[i][j]]), axis=1), axis=1)
tr_loss, tr_acc = model.train_on_batch([input_1, input_2], y_true)#np.array([y_true]))
model.reset_states()
My model.summary() looks like this:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (1, 1, 5625) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (1, 1, 1) 0
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (1, 1, 5626) 0 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (1, 10) 225480 concatenate_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (1, 5625) 61875 lstm_1[0][0]
==================================================================================================
Total params: 287,355
Trainable params: 287,355
Non-trainable params: 0
__________________________________________________________________________________________________
I am working with Keras version 2.1.2 with the TensorFlow backend (TensorFlow version 1.4.0. How can I resolve the ValueError?

Answer 1

It turns out I needed to address the target, as the ValueError implied.
If you replace:
y_true = y_train[i,:]
with:
y_true_1 = np.expand_dims(y_train[i,:], axis=1)
y_true = np.swapaxes(y_true_1, 0, 1)
The code runs.