Trained_Classifier_ANN.py

import pandas as pd
import cv2
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras import optimizers
from keras.models import model_from_json
from keras.layers import Dropout, Dense
from keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
from keras import callbacks
from sklearn.preprocessing import StandardScaler


tbCallBack = callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)

#train
df = pd.read_csv('anglesratiostrain.csv', header = None)
X_train = df.iloc[:,0:94]
y_train = df.iloc[:,-1:]
enc = LabelEncoder()
enc.fit(y_train)
y_train = enc.transform(y_train)
y_train = to_categorical(y_train)
y_train = pd.DataFrame(y_train)
scaler = StandardScaler(with_mean = False).fit(X_train)
X_train = scaler.transform(X_train)
scalery = StandardScaler(with_mean = False).fit(y_train)
y_train = scalery.transform(y_train)

#test
df = pd.read_csv('anglesratiostest.csv', header = None)
X_test = df.iloc[:,0:94]
y_test = df.iloc[:,-1:]
enc.fit(y_test)
y_test = enc.transform(y_test)
y_test = to_categorical(y_test)
y_test = pd.DataFrame(y_test)
X_test = scaler.transform(X_test)
y_test = scalery.transform(y_test)



#predictions
df = pd.read_csv('anglesratiospredictions.csv', header = None)

X_pred = df.iloc[:,0:94]

y_pred = df.iloc[:,-1:]

enc.fit(y_pred)

y_pred = enc.transform(y_pred)

y_pred = to_categorical(y_pred)

y_pred = pd.DataFrame(y_pred)

X_pred = scaler.transform(X_pred)

y_pred = scalery.transform(y_pred)



print "Model Started"


json_file = open('classifier.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("classifier.h5")
print("Loaded model from disk")
 
# evaluate loaded model on test data
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])


loss,score = model.evaluate(X_test, y_test, batch_size=200, verbose=1)

print("test acc: "+ str(score*100))

print("test loss: "+ str(loss))


loss,score = model.evaluate(X_pred, y_pred, batch_size=200, verbose=1)

print("pred acc: "+ str(score*100))

print("pred loss: "+ str(loss))