ryanleeallred

Error when I try to run the mysql server:


Last login: Mon Feb 17 10:22:15 on ttys000
Ryans-MacBook-Pro-2:devmounta.in-www ryanallred$ mysql
ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/tmp/mysql.sock' (2)
Ryans-MacBook-Pro-2:devmounta.in-www ryanallred$ mysql.server start
Starting MySQL
.................................................................................................... ERROR! The server quit without updating PID file (/usr/local/var/mysql/Ryans-MacBook-Pro-2.local.pid).
Ryans-MacBook-Pro-2:devmounta.in-www ryanallred$ 

ryanleeallred

# input image dimensions
img_rows, img_cols = 32, 32   

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = cifar10.load_data()   

#Only look at cats [=3] and dogs [=5]
train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  
test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     

ryanleeallred

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras import backend as K
import matplotlib
from matplotlib import pyplot as plt
import numpy as np

#Input image dimensions
img_rows, img_cols = 32, 32

ryanleeallred

# Rotate images by 90 degrees
datagen = ImageDataGenerator(rotation_range=90)

# fit parameters from data
datagen.fit(x_train)

# Configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):
    # Show 9 images
    for i in range(0, 9):

ryanleeallred

# Flip images vertically
datagen = ImageDataGenerator(vertical_flip=True)

# fit parameters from data
datagen.fit(x_train)

# Configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):
    # Show 9 images
    for i in range(0, 9):

ryanleeallred

# Shift images vertically or horizontally 
# Fill missing pixels with the color of the nearest pixel
datagen = ImageDataGenerator(width_shift_range=.2, 
                             height_shift_range=.2,
                             fill_mode='nearest')

# fit parameters from data
datagen.fit(x_train)

# Configure batch size and retrieve one batch of images

ryanleeallred

# Import skimage modules
from skimage import data, img_as_float
from skimage import exposure

# Lets try augmenting a cifar10 image using these techniques
from skimage import data, img_as_float
from skimage import exposure

# Load an example image from cifar10 dataset
img = images[0]

ryanleeallred

def __init__(self,
 contrast_stretching=False, #####
 histogram_equalization=False, #####
 adaptive_equalization=False, #####
 featurewise_center=False,
 samplewise_center=False,
 featurewise_std_normalization=False,
 samplewise_std_normalization=False,
 zca_whitening=False,
 rotation_range=0.,

ryanleeallred

def random_transform(self, x):
  img_row_axis = self.row_axis - 1
  img_col_axis = self.col_axis - 1
  img_channel_axis = self.channel_axis - 1
# use composition of homographies
# to generate final transform that needs to be applied
  if self.rotation_range:
     theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range)
  else:
     theta = 0

ryanleeallred

# Initialize Generator
datagen = ImageDataGenerator(contrast_stretching=True, adaptive_equalization=True, histogram_equalization=True)

# fit parameters from data
datagen.fit(x_train)

# Configure batch size and retrieve one batch of images
for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):
    # Show the first 9 images
    for i in range(0, 9):