Category: Misc

I’m trying to run a Jupyter notebook from this repository: https://github.com/artemyk/ibsgd. Specifically the MNIST_SaveActivations.ipynb file. Problem is the code is from two years ago, and The file was written in tensorflow 2.1.0. The notebook runs fine until the last cell, where I get the following error (see pictures below).

Error

loggingreporter.py file where the error occurs

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How inputs variable is used in loggingreporter.py

I’ve been looking online but I can’t figure out how to fix this attribute error for the version of TensorFlow I’m running (2.8.0). Any guidance is appreciated! I’ve already tried downgrading to 2.1.0 but have not had success. I’m using the latest version of python3

Hi,

i am trying to develop an image recognition app and i already had greate results with a 2 category dataset but the moment i add a third category the accuracy wont increase. Can anybody help me with that?

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle
import time
import numpy as np
pickle_in = open(“X.pickle”,”rb”)
X = pickle.load(pickle_in)
pickle_in = open(“Y.pickle”,”rb”)
Y = pickle.load(pickle_in)
X = X/255.0
Y = np.array(Y)
#parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters
dense_layers = [0, 1, 2, 3]
layer_sizes = [32, 64, 128]
conv_layers = [1, 2, 3]
epochs = 10
batch_size = 32
validation_split = 0.3
PROJEKT_NAME=”3″
#parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters-parameters

for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:

NAME = f”CONV{conv_layer}_size{layer_size}_dense{dense_layer}_{time.time()}”
print(NAME)
model = Sequential()
model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
for l in range(conv_layer-1):
model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
for _ in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation(‘relu’))
model.add(Dense(1))
model.add(Activation(‘sigmoid’))
tensorboard = TensorBoard(log_dir=f”logs/{PROJEKT_NAME}/{NAME}”)
model.compile(loss=’binary_crossentropy’,
optimizer=’adam’,
metrics=[‘accuracy’],
)
model.fit(X, Y,
batch_size=batch_size,
epochs=epochs,
validation_split=validation_split,
callbacks=[tensorboard])

epoch_loss

epoch_accuracy