tensorflow error: ValueError: Input 0 of layer sequential is incompatible with the layer expected axis -1 - python-3.x

Question

I am trying to modify code provided to me to import a image file and build a training and test set using keras.
I am receiving the following error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-154-b4983c6bd066> in <module>()
1 # Fit the model
----> 2 history = model.fit(X_train, y_train, batch_size = 256, epochs = 15, verbose=2, validation_data=(X_test,y_test))
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args, **kwds))
698
699 def invalid_creator_scope(*unused_args, **unused_kwds):
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\training.py:747 train_step
y_pred = self(x, training=True)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\base_layer.py:976 __call__
self.name)
C:\Users\synar\AppData\Roaming\Python\Python37\site-packages\tensorflow\python\keras\engine\input_spec.py:216 assert_input_compatibility
' but received input with shape ' + str(shape))
ValueError: Input 0 of layer sequential_41 is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None, 1]
My code I have implemented so is:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import model_selection
from scipy.io import loadmat
data = loadmat('notMNIST_small.mat')
X_temp = data['images']/255
#for i in range(X_temp.shape[2]):
X = np.empty(shape=[X_temp.shape[2]] + [784], dtype='float32')
for i in range(X_temp.shape[2]):
X[i,:] = X_temp[:,:,i].flatten()
y = pd.get_dummies(data['labels']).to_numpy()
print(X_temp.shape)
print(X.shape)
print(y.shape)
X[1,:]
X = np.array(data['labels']).reshape(-1, 1)
y = np.array(data['labels'])
X_train, X_test, y_train, y_test =train_test_split(
X, y, test_size=0.2, random_state=9)
stdscaler = preprocessing.StandardScaler().fit(X_train)
X_train_scaled = stdscaler.transform(X_train)
X_test_scaled = stdscaler.transform(X_test)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.regularizers import l2, l1
from tensorflow.keras.optimizers import SGD
# Stochastic Logistic Regression
model = Sequential()
# Model
model.add(Dense(units=10, input_shape = [784,], activation = 'relu', kernel_regularizer=l2(0)))
model.add(Dense(units = 40, activation = 'relu'))
model.add(Dense(units = 10, activation = 'sigmoid'))
# Compile model
sgd = SGD(lr=0.1)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
I get the error when implementing the following cell:
# Fit the model
history = model.fit(X_train, y_train, batch_size = 256, epochs = 15, verbose=2, validation_data=(X_test,y_test))
Any help in resolving this would be great I am new to machine learning so excuse my ignorance.

Answer 1

Add extra dimension to your X_train as
x_train = x_train.reshape(-1, 28*28)
model = Sequential()
# Model
model.add(Dense(units=10, input_dim = 784, activation = 'relu', kernel_regularizer=l2(0)))
model.add(Dense(units = 40, activation = 'relu'))
model.add(Dense(units = 10, activation = 'sigmoid'))
# Compile model
sgd = SGD(lr=0.1)
model.compile(loss='categorical_crossentropy', optimizer=sgd)