python - permission denied when trying to read csv file

I'm trying to build a neural net for a school project. I went with a popular model, its a program that determines diabetes risk. The program is based off of the popular 'pima indians diabetes' data set. I have keras and tensorflow set up, but I cant get the program to read the file, it always says "permission denied'.

I've tried updating permissions to 'everyone' but that doesn't work. I'm pretty sure the program can find the file, as they are both inside the same folder. Or maybe that's whats causing the problem? Either way, I'm pretty new to programming in general. any and all help would be greatly appreciated. Below is my code and attached is the console and my file location/contents. thanks.

EDIT: correct picture of console and file location

from keras.models import Sequential

from keras.layers import Dense, Dropout

from sklearn.model_selection import train_test_split

import numpy



# random seed for reproducibility

numpy.random.seed(2)



# load up the data

#dataset = numpy.loadtxt("dataset.csv", delimiter=",")

dataset = open("dataset.csv")





# split into input (X) and output (Y) variables, (split the data, ouput is 0 or 1)

X = dataset[:,0:8]

Y = dataset[:,8]



# split X, Y into a train and test set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)



# create model, add layers (specify the function)

model = Sequential()

model.add(Dense(15, input_dim=8, activation='relu')) # input layer requires input_dim param

model.add(Dense(10, activation='relu'))

model.add(Dense(8, activation='relu'))

model.add(Dropout(.2))

model.add(Dense(1, activation='sigmoid')) # sigmoid instead of relu for final probability between 0 and 1



# compile the model, adam gradient descent model (optimized)

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])



# function for fitting the data (training the network)

model.fit(x_train, y_train, epochs = 1000, batch_size=20, validation_data=(x_test, y_test))



# save it

model.save('weights.h5')

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

add a comment |

EDIT: correct picture of console and file location

from keras.models import Sequential

from keras.layers import Dense, Dropout

from sklearn.model_selection import train_test_split

import numpy



# random seed for reproducibility

numpy.random.seed(2)



# load up the data

#dataset = numpy.loadtxt("dataset.csv", delimiter=",")

dataset = open("dataset.csv")





# split into input (X) and output (Y) variables, (split the data, ouput is 0 or 1)

X = dataset[:,0:8]

Y = dataset[:,8]



# split X, Y into a train and test set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)



# create model, add layers (specify the function)

model = Sequential()

model.add(Dense(15, input_dim=8, activation='relu')) # input layer requires input_dim param

model.add(Dense(10, activation='relu'))

model.add(Dense(8, activation='relu'))

model.add(Dropout(.2))

model.add(Dense(1, activation='sigmoid')) # sigmoid instead of relu for final probability between 0 and 1



# compile the model, adam gradient descent model (optimized)

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])



# function for fitting the data (training the network)

model.fit(x_train, y_train, epochs = 1000, batch_size=20, validation_data=(x_test, y_test))



# save it

model.save('weights.h5')

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

add a comment |

EDIT: correct picture of console and file location

from keras.models import Sequential

from keras.layers import Dense, Dropout

from sklearn.model_selection import train_test_split

import numpy



# random seed for reproducibility

numpy.random.seed(2)



# load up the data

#dataset = numpy.loadtxt("dataset.csv", delimiter=",")

dataset = open("dataset.csv")





# split into input (X) and output (Y) variables, (split the data, ouput is 0 or 1)

X = dataset[:,0:8]

Y = dataset[:,8]



# split X, Y into a train and test set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)



# create model, add layers (specify the function)

model = Sequential()

model.add(Dense(15, input_dim=8, activation='relu')) # input layer requires input_dim param

model.add(Dense(10, activation='relu'))

model.add(Dense(8, activation='relu'))

model.add(Dropout(.2))

model.add(Dense(1, activation='sigmoid')) # sigmoid instead of relu for final probability between 0 and 1



# compile the model, adam gradient descent model (optimized)

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])



# function for fitting the data (training the network)

model.fit(x_train, y_train, epochs = 1000, batch_size=20, validation_data=(x_test, y_test))



# save it

model.save('weights.h5')

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

EDIT: correct picture of console and file location

from keras.models import Sequential

from keras.layers import Dense, Dropout

from sklearn.model_selection import train_test_split

import numpy



# random seed for reproducibility

numpy.random.seed(2)



# load up the data

#dataset = numpy.loadtxt("dataset.csv", delimiter=",")

dataset = open("dataset.csv")





# split into input (X) and output (Y) variables, (split the data, ouput is 0 or 1)

X = dataset[:,0:8]

Y = dataset[:,8]



# split X, Y into a train and test set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)



# create model, add layers (specify the function)

model = Sequential()

model.add(Dense(15, input_dim=8, activation='relu')) # input layer requires input_dim param

model.add(Dense(10, activation='relu'))

model.add(Dense(8, activation='relu'))

model.add(Dropout(.2))

model.add(Dense(1, activation='sigmoid')) # sigmoid instead of relu for final probability between 0 and 1



# compile the model, adam gradient descent model (optimized)

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])



# function for fitting the data (training the network)

model.fit(x_train, y_train, epochs = 1000, batch_size=20, validation_data=(x_test, y_test))



# save it

model.save('weights.h5')

python tensorflow permissions denied

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

edited Nov 25 '18 at 4:52

asked Nov 25 '18 at 4:47

Al Capwned

asked Nov 25 '18 at 4:47

Al Capwned

asked Nov 25 '18 at 4:47

Al Capwned

add a comment |

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