Input 0 of layer time_distributed_30 is incompatible with the layer: expected ndim=5, found ndim=4. Full shape received: (None, None, None, None)

Question

1. I am trying for classification of MSTAR data set with 10 classes
2. I have used the modal that contains DCNN and BILSTM with 15 time steps

My questions are:

1. How to overcome the error
2. How to get the good classification results.

My code is:

inputs=Input(shape=(15,60,60,3))
model = Sequential()
# 1st Convolutional Layer
model.add(TimeDistributed(Conv2D(filters=16 ,kernel_size=(5,5), padding='valid'),input_shape=(15,60,60,3)))
model.add(TimeDistributed(Activation('relu')))
# Batch Normalisation
model.add(TimeDistributed(BatchNormalization()))
# Pooling 
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')))

# 2nd Convolutional Layer
model.add(TimeDistributed(Conv2D(filters=32, kernel_size=(5,5), padding='valid')))
model.add(TimeDistributed(Activation('relu')))
# Batch Normalisation
model.add(TimeDistributed(BatchNormalization()))
# Pooling
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')))

# 3rd Convolutional Layer
model.add(TimeDistributed(Conv2D(filters=64, kernel_size=(5,5), padding='valid')))
model.add(Activation('relu'))
# Batch Normalisation
model.add(TimeDistributed(BatchNormalization()))
# Pooling
model.add(TimeDistributed(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid')))

# 4th Convolutional Layer
model.add(TimeDistributed(Conv2D(filters=128, kernel_size=(4,4), padding='valid')))
model.add(TimeDistributed(Activation('relu')))
# Batch Normalisation
model.add(TimeDistributed(BatchNormalization()))
model.add(TimeDistributed(Flatten()))
#add dropout
model.add(Dropout(0.0))
#bidirectional lstm
model.add(Bidirectional(LSTM(1024,activation='tanh',return_sequences=True)))
#2 nd bidirectional layer
model.add(Bidirectional(LSTM(1024,activation='tanh',return_sequences=False)))
# Output Layer
model.add(Dense(10))
model.add(Activation('softmax'))

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam',\
 metrics=['accuracy'])
model.summary()