InvalidArgumentError: Graph execution error: with cats vs dogs program

Question

so I am new to machine learning, and I was building a cats and dogs program from a youtube tutorial when I came across this error. I have already looked through the video and confirmed that our code is the same. However, I am using google colab while she is using a jupyter notebook. Is this the issue, or do I need to change my code?

code

model = Sequential([
    Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(244, 224, 3)),
    MaxPool2D(pool_size=(2,2), strides = 2),
    Conv2D(filters=64, kernel_size=(3, 3), activation = 'relu', padding = 'same'),
    MaxPool2D(pool_size=(2,2), strides = 2),
    Flatten(),
    Dense(units=2, activation='softmax'),
])
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics =['accuracy'])
model.fit(x=train_batches, validation_data=valid_batches, epochs=10, verbose=2)

error

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-155-2c9b6572eabb> in <module>()
----> 1 model.fit(x=train_batches, validation_data=valid_batches, epochs=1, verbose=2)

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     53     ctx.ensure_initialized()
     54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
     57     if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node 'sequential_16/flatten_16/Reshape' defined at (most recent call last):
    File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
      "__main__", mod_spec)
    File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
      exec(code, run_globals)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
      app.start()
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
      self.io_loop.start()
    File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
      self.asyncio_loop.run_forever()
    File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
      self._run_once()
    File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
      handle._run()
    File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
      self._context.run(self._callback, *self._args)
    File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
      handler_func(fileobj, events)
    File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 452, in _handle_events
      self._handle_recv()
    File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv
      self._run_callback(callback, msg)
    File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback
      callback(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
      return self.dispatch_shell(stream, msg)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
      handler(stream, idents, msg)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
      user_expressions, allow_stdin)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
      interactivity=interactivity, compiler=compiler, result=result)
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
      if self.run_code(code, result):
    File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "<ipython-input-155-2c9b6572eabb>", line 1, in <module>
      model.fit(x=train_batches, validation_data=valid_batches, epochs=1, verbose=2)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
      tmp_logs = self.train_function(iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function
      return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step
      outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step
      y_pred = self(x, training=True)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
      return super(Sequential, self).call(inputs, training=training, mask=mask)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 452, in call
      inputs, training=training, mask=mask)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
      return fn(*args, **kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py", line 96, in call
      return tf.reshape(inputs, flattened_shape)
Node: 'sequential_16/flatten_16/Reshape'
Input to reshape is a tensor with 2381440 values, but the requested shape requires a multiple of 218624
     [[{{node sequential_16/flatten_16/Reshape}}]] [Op:__inference_train_function_9593]

Answer 1

You must change the input shape from (244, 224, 3) to (224, 224, 3)

Replace this

Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(244, 224, 3)),

with this

Conv2D(filters=32, kernel_size=(3,3,), activation = 'relu', padding = 'same', input_shape=(224, 224, 3)),

Let us know if the issue still persists. Thanks!