Das deutsche Python-Forum

Code: Alles auswählen

import pickle
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout
from keras.layers.normalization import BatchNormalization
from keras.utils import to_categorical
import tensorflow as tf
import keras
import numpy as np
import cv2
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # dont show warnings from Tensorflow

testing_image_path = "data/test.p"
training_image_path = "data/train.p"
validation_image_path = "data/valid.p"

with open(training_image_path, mode='rb') as file:
    train = pickle.load(file)
with open(testing_image_path, mode='rb') as file:
    test = pickle.load(file)
with open(validation_image_path, mode='rb') as file:
    valid = pickle.load(file)

X_train, y_train = train['features'], train['labels']
X_test, y_test = test['features'], test['labels']
X_valid, y_valid = valid['features'], valid['labels']

print("Trainingsdaten:", len(X_train))
print("Testdaten:", len(y_test))
print("Validierungsdaten:", len(X_valid))
print("Bilddimensionen:", np.shape(X_train[1]))
print("Anzahl der Klassen:", len(np.unique(y_train)))

n_classes = 43

model = Sequential()

model.add(Dense(64, activation='relu', input_shape=(32, 32, 3,)))
model.add(BatchNormalization())

model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Flatten())

model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())

model.add(Dense(n_classes, activation='softmax'))

model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

X_train_baseline = X_train.reshape(len(X_train), 32, 32, 3).astype('float32')
X_valid_baseline = X_valid.reshape(len(X_valid), 32, 32, 3).astype('float32')
y_train_baseline = keras.utils.to_categorical(y_train, n_classes)
y_valid_baseline = keras.utils.to_categorical(y_valid, n_classes)

model.fit(X_train_baseline, y_train_baseline, batch_size=128, epochs=10, verbose=1, validation_data=(X_valid_baseline, y_valid_baseline))

X_test_baseline = X_test.reshape(len(X_test), 32, 32, 3).astype('float32')
y_test_baseline = keras.utils.to_categorical(y_test, n_classes)

model.evaluate(X_test_baseline, y_test_baseline, verbose=0)

model.save('traffic_signs_100epochs.h5', save_format='h5')

Code: Alles auswählen

PS C:\Users\felix\Desktop\traffic_helper> & D:/Programme/Anaconda/python.exe c:/Users/felix/Desktop/traffic_helper/trafficnet.py
Using TensorFlow backend.
Traceback (most recent call last):
  File "c:/Users/felix/Desktop/traffic_helper/trafficnet.py", line 2, in <module>
    from keras.models import Sequential
  File "D:\Programme\Anaconda\lib\site-packages\keras\__init__.py", line 3, in <module>
    from . import utils
  File "D:\Programme\Anaconda\lib\site-packages\keras\utils\__init__.py", line 6, in <module>
    from . import conv_utils
  File "D:\Programme\Anaconda\lib\site-packages\keras\utils\conv_utils.py", line 9, in <module>
    from .. import backend as K
  File "D:\Programme\Anaconda\lib\site-packages\keras\backend\__init__.py", line 89, in <module>
    from .tensorflow_backend import *
  File "D:\Programme\Anaconda\lib\site-packages\keras\backend\tensorflow_backend.py", line 5, in <module>
    import tensorflow as tf
ModuleNotFoundError: No module named 'tensorflow'

Code: Alles auswählen

import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout
from tensorflow.keras.layers.normalization import BatchNormalization
from tensorflow.keras.utils import to_categorical

import numpy as np
import cv2
import os