I am working with project where model take 3D cuboid as input, and reconstruct 3D cuboid. I am getting error:
ValueError: Can not squeeze dim[4], expected a dimension of 1, got 3 for '{{node Squeeze}} =
Squeeze[T=DT_FLOAT, squeeze_dims=[-1]](remove_squeezable_dimensions/Squeeze)' with input
shapes: [1,227,227,10,3].
I am using below model. Input is 10 consucative frames with height and width 227*227 and three channel(R, G, B)
model=Sequential()
model.add(Conv3D(filters=128,kernel_size=(11,11,1),strides=(4,4,1),padding='valid',input_shape=(227,227,10,3),activation='tanh'))
model.add(Conv3D(filters=64,kernel_size=(5,5,1),strides=(2,2,1),padding='valid',activation='tanh'))
model.add(ConvLSTM2D(filters=64,kernel_size=(3,3),strides=1,padding='same',dropout=0.4,return_sequences=True,recurrent_dropout=0.3))
model.add(ConvLSTM2D(filters=32,kernel_size=(3,3),strides=1,padding='same',dropout=0.3,return_sequences=True))
model.add(ConvLSTM2D(filters=64,kernel_size=(3,3),strides=1,padding='same',dropout=0.5,return_sequences=True))
model.add(Conv3DTranspose(filters=128,kernel_size=(5,5,1),strides=(2,2,1),padding='valid',activation='tanh'))
model.add(Conv3DTranspose(filters=3,kernel_size=(11,11,1),strides=(4,4,1),padding='valid',activation='tanh'))
model.compile(optimizer='adam',loss='mean_squared_error',metrics=['accuracy'])
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv3d (Conv3D) (None, 55, 55, 10, 128) 46592
conv3d_1 (Conv3D) (None, 26, 26, 10, 64) 204864
conv_lstm2d (ConvLSTM2D) (None, 26, 26, 10, 64) 295168
conv_lstm2d_1 (ConvLSTM2D) (None, 26, 26, 10, 32) 110720
conv_lstm2d_2 (ConvLSTM2D) (None, 26, 26, 10, 64) 221440
conv3d_transpose (Conv3DTra (None, 55, 55, 10, 128) 204928
nspose)
conv3d_transpose_1 (Conv3DT (None, 227, 227, 10, 3) 46467
ranspose)
=================================================================
Total params: 1,130,179
Trainable params: 1,130,179
Non-trainable params: 0
_________________________________________________________________
And I am using this code to create dataset and train the model
training_data = []
target_data = []
training_data=np.load('/content/training.npy')
print(training_data.shape) #(227, 227, 3, 14943)
frames=training_data.shape[3]
frames=frames-frames%10
training_data=training_data[:,:,:,:frames]
training_data=training_data.reshape(-1,227,227,10,3)
training_data=np.expand_dims(training_data,axis=5)
target_data=training_data.copy()
epochs=15
batch_size=1
callback_save = ModelCheckpoint("saved_model.h5", monitor="mean_squared_error", save_best_only=True)
callback_early_stopping = EarlyStopping(monitor='loss', patience=3)
history = model.fit(training_data,target_data, batch_size=batch_size, epochs=epochs, callbacks = [callback_save,callback_early_stopping])
model.save("saved_model.h5")
Is training_data=np.expand_dims(training_data,axis=5) needed? I think it makes the training data shape incompatible with the input shape.