| # from tensorflow.keras.preprocessing.image import ImageDataGenerator | |
| # import numpy as np | |
| # import tensorflow as tf | |
| # valid_datagen = ImageDataGenerator( | |
| # rescale=1./255 # Rescaling factor | |
| # ) | |
| # valid_dir = "/Users/rosh/Downloads/Validation_data" | |
| # valid_data = valid_datagen.flow_from_directory(directory=valid_dir, | |
| # batch_size=32, | |
| # target_size=(224, 224), | |
| # class_mode="categorical", | |
| # seed=42) | |
| # loaded_model = tf.keras.models.load_model('improved_model_4.h5') | |
| # true_labels = [] | |
| # for i in range(len(valid_data)): | |
| # _, labels = valid_data[i] | |
| # true_labels.extend(np.argmax(labels, axis=1)) | |
| # | |
| # # Print true labels | |
| # print("True labels:", true_labels) | |
| # pred_prob = loaded_model.predict(valid_data) | |
| # preds = pred_prob.argmax(axis=1) | |
| # print("Predicted: ") | |
| # count = 0 | |
| # for i in range(len(preds)): | |
| # if true_labels[i] == preds[i]: | |
| # count += 1 | |
| # print(count) | |
| #print(tf.keras.models.load_model('model_4_improved_1.h5').summary()) | |
| import keras | |
| import tensorflow as tf | |
| print("Keras version:", keras.__version__) | |
| print("TensorFlow version:", tf.__version__) | |