import tensorflow as tf
import numpy as np
from tensorflow.contrib.learn.python import SKCompat
iris = np.loadtxt('Data/Data/iris_softmax.csv',
delimiter=',',
dtype=np.float32)
x = iris[:, :-3]
y = iris[:, -3:]
y = np.argmax(y, axis=1)
feature_columns = [tf.feature_column.numeric_column('', shape=[5])]
clf = tf.contrib.learn.DNNClassifier(hidden_units=[10, 20, 5],
feature_columns=feature_columns,
n_classes=3)
clf = SKCompat(clf)
clf.fit(x=x, y=y, max_steps=1000)
print(clf.score(x=x, y=y))
# Train the model
mnist_classifier.fit(x=train_data,
y=train_label_sub,
batch_size=128,
steps=400,
monitors=[logging_hook])
# Configure the accuracy metric for evaluation
metrics = {
"accuracy":
learn.MetricSpec(metric_fn=tf.metrics.accuracy,
prediction_key="classes"),
}
# Evaluate the model and print results
eval_results = mnist_classifier.score(x=test_data,
y=test_label_sub,
metrics=metrics,
batch_size=128)
print("************************************************************")
print("epoch: " + str(i + 1))
print(eval_results)
print("************************************************************")
#print (train_data.shape)
#print (train_data[1])
#print (test_label_super)
#print (test_label_sub)
#np.savetxt('ytrain1', y_train)
def main(unused_arguments):
localtime = time.asctime(time.localtime(time.time()))
print(localtime)
content = np.loadtxt('train_data')
train_data = []
temp = 0
tmp = []
for x in content:
if temp == 255:
tmp.append(x)
train_data.append(tmp)
temp = 0
tmp = []
else:
tmp.append(x)
temp += 1
with open('train_labels', 'rb') as fp:
train_labels = pickle.load(fp)
train_data = np.array(train_data, dtype=np.float32)
train_labels = np.array(train_labels, dtype=np.int32)
# Create the Estimator
mnist_classifier = SKCompat(
learn.Estimator(model_fn=trainTypes,
model_dir="/Users/praneet/Downloads/model"))
# Set up logging for predictions
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=50)
# Train the model
mnist_classifier.fit(x=train_data,
y=train_labels,
batch_size=100,
steps=2500,
monitors=[logging_hook])
# Accuracy metric for evaluation
metrics = {
"accuracy":
learn.MetricSpec(metric_fn=tf.metrics.accuracy,
prediction_key="classes"),
}
# Evaluation
print('Evaluation: ')
content = np.loadtxt('eval_data')
eval_data = []
temp = 0
tmp = []
for x in content:
if temp == 255:
tmp.append(x)
eval_data.append(tmp)
temp = 0
tmp = []
else:
tmp.append(x)
temp += 1
with open('eval_labels', 'rb') as fp:
eval_labels = pickle.load(fp)
eval_data = np.array(eval_data, dtype=np.float32)
eval_labels = np.array(eval_labels, dtype=np.int32)
# Evaluate the model
eval_results = mnist_classifier.score(x=eval_data,
y=eval_labels,
metrics=metrics)
print(eval_results)
# Predictions
print('Predictions: ')
evallst = []
outputlst = []
fileCount = 1
evaluatePath = "/Users/praneet/Downloads/test/"
lst = []
lst.append("image_name")
lst.append("Type_1")
lst.append("Type_2")
lst.append("Type_3")
evallst.append(lst)
outputlst.append(lst)
for root, dirs, files in os.walk(evaluatePath):
for fileName in files:
if fileName.endswith(".jpg"):
eval_data = []
filePath = os.path.abspath(os.path.join(root, fileName))
img = cv2.imread(filePath)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = processImage(img)
fixedSize = (256, 256)
img = cv2.resize(img, dsize=fixedSize)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
tmp_data = np.zeros((256, 256), dtype=np.float32)
for x in range(img.shape[0]):
for y in range(img.shape[1]):
tmp_data[x][y] = img[x][y] / 255.0
eval_data.append(tmp_data)
eval_data = np.array(eval_data)
# Predict values for each image
predictions = mnist_classifier.predict(x=eval_data)
print(fileName, predictions)
lst = []
lst.append(fileName)
for x in predictions['probabilities']:
for y in x:
lst.append(y)
outputlst.append(lst)
if lst[1] > 0.25:
lst = []
lst.append(fileName)
lst.append(1)
lst.append(0)
lst.append(0)
if lst[3] > 0.29:
lst = []
lst.append(fileName)
lst.append(0)
lst.append(0)
lst.append(1)
if lst[2] > 0.58:
lst = []
lst.append(fileName)
lst.append(0)
lst.append(1)
lst.append(0)
if lst[1] == 0 or lst[1] == 1:
print("Non Ambiguous Prediction")
evallst.append(lst)
fileCount += 1
print('Total files: ', fileCount)
df = pd.DataFrame(evallst)
df.to_csv('output_normalized.csv', index=False, header=False)
df = pd.DataFrame(outputlst)
df.to_csv('output_integers.csv', index=False, header=False)
localtime = time.asctime(time.localtime(time.time()))
print(localtime)