Getting a long error message, and I don't know what it means. Maybe trying x_train = x_train / 255.0 which affects the model.fit call? The error is at the model.fit line.
from keras.datasets import mnist
import matplotlib.pyplot as plt
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# save input image dimensions
img_rows, img_cols = 28, 28
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
x_train = x_train / 255.0
x_test = x_test / 255.0
from keras.utils import to_categorical
num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=(img_rows, img_cols, 1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
batch_size = 128
epochs = 10
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test)) ***#Error Here***
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save("test_model.h5")
import imageio
import numpy as np
from matplotlib import pyplot as plt
im = imageio.imread("https://i.imgur.com/a3Rql9C.png")
gray = np.dot(im[...,:3], [0.299, 0.587, 0.114])
plt.imshow(gray, cmap = plt.get_cmap('gray'))
plt.show()
# reshape the image
gray = gray.reshape(1, img_rows, img_cols, 1)
# normalize image
gray /= 255
# load the model
from keras.models import load_model
model = load_model("test_model.h5")
# predict digit
prediction = model.predict(gray)
print(prediction.argmax())
Error:
2023-12-01 22:03:39.867667: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Epoch 1/10
Traceback (most recent call last):
File "c:\Users\Jim\Documents\Pyro\number cnn.py", line 48, in <module>
model.fit(x_train, y_train,
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\tensorflow\python\eager\execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last):
File "c:\Users\Jim\Documents\Pyro\number cnn.py", line 48, in <module>
model.fit(x_train, y_train,
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1742, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1338, in train_function
return step_function(self, iterator)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1322, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1303, in run_step
outputs = model.train_step(data)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1081, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\training.py", line 1139, in compute_loss
return self.compiled_loss(
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\losses.py", line 142, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\losses.py", line 268, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\losses.py", line 2354, in sparse_categorical_crossentropy
return backend.sparse_categorical_crossentropy(
File "C:\Users\Jim\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\keras\src\backend.py", line 5762, in sparse_categorical_crossentropy
res = tf.nn.sparse_softmax_cross_entropy_with_logits(
Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
logits and labels must have the same first dimension, got logits shape [128,10] and labels shape [1280]
[[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_1317]
PS C:\Users\Jim\Documents\Pyro>
* History restored
Use CategoricalCrossentropy instead of SparseCategoricalCrossentropy.
As the error message says:
logits and labels must have the same first dimension, got logits shape [128,10] and labels shape [1280]
From Keras SparseCategoricalCrossentropy Docs:
Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss.
Your softmax output size is batch_size x num_classes, but sparse categorical crossentropy is expecting an array of size batch_size, where each class is represented as an integer.