I am facing an error while running my model.
Input to reshape is a tensor with 327680 values, but the requested shape requires a multiple of 25088
I am using (256 * 256) images. I am reading the images from drive in google colab. Can anyone tell me how to get rid of this error?
my colab code:
#defining the path to our data using the pathlib library
data_root = Path('/content/drive/MyDrive/Plant Disease detection/plantdisease_dataset')
print('data_root:', data_root)
Dim = 256 #dimension of the image
batch_size = 10 #batch size specified to be trained
Num_class = 38 #number of classes of the dataset
train_steps_per_epoch = 52424 // batch_size
val_steps_per_epoch = 13097 // batch_size
test_steps_per_epoch = 154 // batch_size
datagen = ImageDataGenerator(rescale = 1./255, validation_split = 0.2)
train_gen = datagen.flow_from_directory(data_root/'/content/drive/MyDrive/Plant Disease detection/plantdisease_dataset', target_size = (Dim, Dim),batch_size = 10,subset = 'training')
val_gen = datagen.flow_from_directory(data_root/'/content/drive/MyDrive/Plant Disease detection/plantdisease_dataset', target_size = (Dim, Dim), batch_size = 10,subset = 'validation')
#Found 52424 images belonging to 39 classes.
#Found 13097 images belonging to 39 classes.
history = model.fit(
train_gen,steps_per_epoch = train_steps_per_epoch,
epochs = 100,
validation_data = val_gen,
validation_steps = val_steps_per_epoch,
)
Error message:
Epoch 1/100
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-49-bb01bdcc02ea> in <module>()
3 epochs = 100,
4 validation_data = val_gen,
----> 5 validation_steps = val_steps_per_epoch,
6 # callbacks = [cb_checkpointer,cb_early_stopper,reducelr, tensorboard_callback]
7 )
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)
57 ctx.ensure_initialized()
58 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 59 inputs, attrs, num_outputs)
60 except core._NotOkStatusException as e:
61 if name is not None:
InvalidArgumentError: Input to reshape is a tensor with 327680 values, but the requested shape requires a multiple of 25088
[[node sequential_1/flatten/Reshape
(defined at /usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py:96)
]] [Op:__inference_train_function_4466]
Errors may have originated from an input operation.
Input Source operations connected to node sequential_1/flatten/Reshape:
In[0] sequential_1/block5_pool/MaxPool (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/pooling.py:362)
In[1] sequential_1/flatten/Const (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py:91)
Operation 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 2822, 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-49-bb01bdcc02ea>", line 5, in <module>
>>> validation_steps = val_steps_per_epoch,
>>>
>>> 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 1216, in fit
>>> tmp_logs = self.train_function(iterator)
>>>
>>> File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 878, in train_function
>>> return step_function(self, iterator)
>>>
>>> File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 867, 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 860, in run_step
>>> outputs = model.train_step(data)
>>>
>>> File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 808, 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 1083, 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 373, 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 1083, 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)
>>>
CodePudding user response：
The problem is the default shape used for the VGG19 model. You can try replacing the input shape and applying a Flatten layer just before the output layer. Here is a working example:
import tensorflow as tf
import pathlib
flowers = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
batch_size = 32
img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, rotation_range=20)
ds = img_gen.flow_from_directory(flowers, batch_size=batch_size, target_size = (256, 256), shuffle=True)
train_steps_per_epoch = len(ds) // batch_size
vgg19_model=tf.keras.applications.vgg19.VGG19(include_top=False, input_tensor=tf.keras.layers.Input(shape=(256, 256, 3)))
model = tf.keras.Sequential()
for layer in vgg19_model.layers[:-1]:
model.add(layer)
for layer in model.layers:
layer.trainable=False
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(5, activation='softmax'))
model.summary()
model.compile(tf.keras.optimizers.Adam(learning_rate=0.0001), loss='categorical_crossentropy',metrics=['accuracy'])
history = model.fit(
ds,
steps_per_epoch = train_steps_per_epoch,
epochs = 100
)
You can also resize your images to the default input shape (224, 224) of the VGG19 model like this and it will also work without a Flatten layer:
flowers = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
batch_size = 32
img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, rotation_range=20)
ds = img_gen.flow_from_directory(flowers, batch_size=batch_size, target_size = (224, 224), shuffle=True)
train_steps_per_epoch = len(ds) // batch_size
vgg19_model=tf.keras.applications.vgg19.VGG19()
model = tf.keras.Sequential()
for layer in vgg19_model.layers[:-1]:
model.add(layer)
for layer in model.layers:
layer.trainable=False
model.add(tf.keras.layers.Dense(5, activation='softmax'))
model.summary()
model.compile(tf.keras.optimizers.Adam(learning_rate=0.0001), loss='categorical_crossentropy',metrics=['accuracy'])
history = model.fit(
ds,
steps_per_epoch = train_steps_per_epoch,
epochs = 100
)