3.6.1-1 causing issues on M1

[ImolaS3]

I was prompted to update to 3.6.1 on my M1 mac and now I can't run several programs that previously worked fine, and they still work on my intel mac running 3.6.1

The code starts to process and after a few seconds, I get an error "Kernel Restarting The kernel appears to have died. It will restart automatically"

This is on code that ran fine before the update

if it helps, one of the programs giving this errors is:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # hide info and warning messages from tensorflow, but not errors
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

data = pd.read_csv('/Users/antonysutton/My Drive/Documents/Design_and_analysis/Data sets/handwritten data set/A_Z Handwritten Data.csv')

feature = data.iloc[:, data.columns != '0'].values
label = data[data.columns[0]].values

x_train, x_test, y_train, y_test = train_test_split(feature, label, test_size=0.2)
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)

print(x_train.shape)

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train / 255.
x_test = x_test / 255.

print(x_train.shape)

model = Sequential([
Conv2D(64, (5, 5), input_shape=(28, 28, 1), activation="relu"),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.25),
Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation="relu"),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.25),
Flatten(),
Dense(64, activation='relu'),
Dropout(0.4),
Dense(26, activation='softmax'),
])

model.compile(
optimizer=keras.optimizers.Adam(learning_rate=0.001),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()]
)

model.fit(x_train, y_train, epochs=20, batch_size=64, validation_data=(x_test, y_test))

def show_example(index, preds, dsx, dsy):
current_img = dsx[index][:, :, 0] * 255
prediction = np.argmax(preds[index])
if(len(dsy) > 0 or dsy != None) :
label = dsy[index]
print("Label:", label)
print("Prediction:", prediction)
plt.imshow(current_img, interpolation='nearest', cmap='gray')
plt.show()

show_example(9, preds, x_test, y_test), show_example(23, preds, x_test, y_test)

def analysis(preds, limit, dsx, dsy):
correct = 0
misclassified = []
for i in range(limit):
prediction = np.argmax(preds[i])
label = dsy[i]
if(prediction == label):
correct += 1
else:
misclassified.append(i)

print(f"Predictions in a limit of {limit} are {(correct / limit) * 100} correct")
print(f"Misclassfied {len(misclassified)} examples:")
for i in misclassified:
    show_example(i, preds, dsx, dsy)

preds = model.predict(x_test)
analysis(preds, x_test.shape[0], x_test, y_test)

def predict(filepath, submitfile):
X_test = pd.read_csv(filepath) # 测试数据存放于 test.cv

X_test = X_test.to_numpy()
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_test = X_test / 255.
preds = model.predict(X_test)
labels = [np.argmax(i) for i in preds]
idxs = [i+1 for i in range(len(labels))]
submit = pd.DataFrame({'ImageId': idxs, 'Label': labels})  # 格式
submit.to_csv(submitfile, index=False)

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[JasonWeill]

What version of JupyterLab Desktop were you previously using on your M1 Mac?

On your M1 Mac, if you uninstall JupyterLab Desktop and install version 3.6.1-1 again, do you still see this problem? I'm curious about whether this is a bug with the upgrade flow.

[JasonWeill]

@ImolaS3 Can you provide the info requested above, please?

[JasonWeill]

Without the info requested above, it's not possible to reproduce and diagnose this bug. Closing this for now; please reopen with the requested information. Thanks!