Why doesn't Python want to read pictures? - scikit-learn

Question

I don’t understand why my program doesn’t read pictures. I tried many different ways, but they only solved a small part of the problems. The main one remained - OSError: Initializing from file failed.
Here is what the program shows after execution (full text):
Traceback (most recent call last):
File "C:\Users\sanch\Downloads\test.py", line 123, in <module>
train_data = pd.read_csv("C:\\Users\\sanch\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\tensorflow\\tensorflow-for-poets-2-master\\tf_files\\test_data", delimiter=',', nrows=nRowsRead)
File "C:\Users\sanch\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\io\parsers.py", line 685, in parser_f
return _read(filepath_or_buffer, kwds)
File "C:\Users\sanch\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\io\parsers.py", line 457, in _read
parser = TextFileReader(fp_or_buf, **kwds)
File "C:\Users\sanch\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\io\parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "C:\Users\sanch\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\io\parsers.py", line 1135, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "C:\Users\sanch\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\io\parsers.py", line 1917, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 382, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 693, in pandas._libs.parsers.TextReader._setup_parser_source
OSError: Initializing from file failed
from mpl_toolkits.mplot3d import Axes3D # For Basic ploting
from sklearn.preprocessing import StandardScaler # Preprocessing
from sklearn import preprocessing # Preprocessing
from random import seed
import tensorflow as tf
from sklearn.naive_bayes import GaussianNB # import gaussian naive bayes model
from sklearn.tree import DecisionTreeClassifier # import Decision tree classifier
from sklearn import metrics # Import scikit - learn metrics module for accuracy calculation
import matplotlib.image as mpimg
import matplotlib.pyplot as plt# plotting
import numpy as np # linear algebra
import os # accessing directory structure
import pandas as pd # data processing, CSV file I / O(e.g.pd.read_csv)
for dirname, _, filenames in os.walk("C:\\Users\\sanch\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\tensorflow\\tensorflow-for-poets-2-master\\tf_files\\test_data"):
for filename in filenames:
print(os.path.join(dirname))
def create_model(lyrs=[8], act='linear', opt='Adam', dr=0.0):
# set random seed for reproducibility
seed(42)
tf.random.set_seed(42)
model = tf.keras.Sequential()
# create first hidden layer
model.add(tf.keras.Dense(lyrs[0], input_dim=X_train.shape[1], activation=act))
# create additional hidden layers
for i in range(1, len(lyrs)):
model.add(tf.keras.Dense(lyrs[i], activation=act))
# add dropout, default is none
model.add(tf.keras.Dropout(dr))
# create output layer
model.add(tf.keras.Dense(1, activation='sigmoid')) # output layer
model.complete(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
def create_model(lyrs=[5], act='linear', opt='Adam', dr=0.0):
# set random seed for reproducibility
seed(42)
tf.random.set_seed(42)
model = tf.keras.Sequential()
# create first hidden layer
model.add(tf.keras.Dense(lyrs[0], input_dim=X_train.shape[1], activation=act))
# create additional hidden layers
for i in range(1, len(lyrs)):
model.add(tf.keras.Dense(lyrs[i], activation=act))
# add dropout, default is none
model.add(tf.keras.Dropout(dr))
# create output layer
model.add(tf.keras.Dense(1, activation='sigmoid')) # output layer
model.complete(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
model = create_model()
print(model.summary())
# train model on full train set, witch 80/20 CV split
trainig = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=0)
val_acc = np.mean(trainig.history['val_acc'])
print("\n%s: %.2f%%" % ('val_acc', val_acc * 100))
model = create_model()
print(model.summary())
# train model on full train set, witch 80/20 CV split
trainig = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=0)
val_acc = np.mean(trainig.history['val_acc'])
print("\n%s: %.2f%%" % ('val_acc', val_acc * 100))
def create_model(lyrs=[10], act='linear', opt='Adam', dr=0.0):
# set random seed for reproducibility
seed(42)
tf.random.set_seed(42)
model = tf.keras.Sequential()
# create first hidden layer
model.add(tf.keras.Dense(lyrs[0], input_dim=X_train.shape[1], activation=act))
# create additional hidden layers
for i in range(1, len(lyrs)):
model.add(tf.keras.Dense(lyrs[i], activation=act))
# add dropout, default is none
model.add(tf.keras.Dropout(dr))
# create output layer
model.add(tf.keras.Dense(1, activation='sigmoid')) # output layer
model.complete(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
model = create_model()
print(model.summary())
# train model on full train set, witch 80/20 CV split
trainig = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=0)
val_acc = np.mean(trainig.history['val_acc'])
print("\n%s: %.2f%%" % ('val_acc', val_acc * 100))
# create model
model = tf.keras.KerasClassifier(build_nf=create_model, verbose=0)
# define the grid search parameters
batch_size = [16, 32, 64]
epochs = [50, 100]
param_grid = dict(batch_size=batch_size, ephochs=epochs)
# search the grid
grid = tf.keras.GridSearchCV(estimator=model,
param_grid=param_grid,
cv=3,
verbose=2) # include n_jobs=-1 if you are using CPU
grid_result = grid.fit(X_train, y_train)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_["params"]
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
if __name__ == '__main__':
nRowsRead = None # specify No.of row. 'None' for whole data
# test_mosaic.scv may have more rows in reality, but we are only loading / previewing the first 1000 rows
train_data = pd.read_csv("C:\\Users\\sanch\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\tensorflow\\tensorflow-for-poets-2-master\\tf_files\\test_data", delimiter=',', nrows=nRowsRead)
nRow, nCol = train_data.shape
print(f'Yhere are {nRow} rows and {nCol} columns')
X_train = train_data.drop('Label', axis=1)
X_test = test_data.drop('Label', axis=1)
y_train = train_data['Label']
y_test = test_data['Label']
Perhaps I did not write everything correctly, I do not know. Please help, I have very little time to show this project. I don't know what to do anymore.