Keras multi-label time-series classification considering time-series as an input image vector

I am trying to build a multi-class classifier using Keras. I am not quite sure I have implemented it correctly.
Data is like this

label time-series variables [0:25728}

index 0  1   2   3   4            25728
  0   1  2.5 3.2 1.6 1.05 ........ 2.54
  1   5  3.2 1.6 1.5 1.49 ........ 1.41
  2   1  2.3 3.2 1.5 1.52 ........ 2.11
  3   3  0.2 3.1 1.5 1.89 ........ 0.81
  4   8  1.2 1.1 0.2 1.19 ........ 3.71
  .   5  .    .   .   .   ........   .
  .   7  .    .   .   .   ........   .
1323  5  .    .   .   .   ........   .

Here is the code.
I split data by 68 % then reshaping 1D array to a 2D array.
as 384*67 = 25728
So forming an image of vector 384 by 67 for one label

def readucr(filename):
data = np.loadtxt(filename, delimiter=',')
Y = data[:, 0]
X = data[:, 1:]
return X, Y

x_train, a = readucr(path+'p2_TRAIN')
x_test, b = readucr(path+'p2_TEST')
df_train_y = pd.read_csv(path+'p2_TRAIN',header=None)
df_test_y = pd.read_csv(path+'p2_TEST',header=None)

x_train = x_train[:,0:25728]
x_test = x_test[:,0:25728]

scaler = MinMaxScaler(feature_range=(0, 1))
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)

x_train =x_train.reshape(x_train.shape[0],384,67)
x_test =x_test.reshape(x_test.shape[0],384,67)

train_label_y = df_train_y[0].values
test_label_y = df_test_y[0].values
batch_size = min(x_train.shape[0] / 10, 10)

y_train = np_utils.to_categorical(train_label_y)
y_test = np_utils.to_categorical(test_label_y)

x_train = x_train.reshape(x_train.shape + (1,))
x_test = x_test.reshape(x_test.shape + (1,))

input_shape = x_train.shape[1:]
model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3), padding='same',
                 input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(8, kernel_size=(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(GlobalAveragePooling2D())
model.add(Dense(9, activation='softmax'))

optimizer = keras.optimizers.Adam()
model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=nb_epochs, verbose=1)
score = model.evaluate(x_test, Y_test)
print("Accuracy: %.2f%%" % (score[1] * 100))

It gives 96.16% accuracy but I don’t believe it is true.
I want to predict the labels.

1. How can I predict labels?
2. What I am doing wrong?

Please help! Thank you.