79 lines
1.7 KiB
Python
79 lines
1.7 KiB
Python
# %% [markdown]
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# # Import the required libraries
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# %%
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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batch_size=64
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img_height=224
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img_width=224
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import os
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os.environ["CUDA_VISIBLE_DEVICES"]="0"
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train_ds = tf.keras.utils.image_dataset_from_directory(
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"combined_data_skeleton/",
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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test_ds = tf.keras.utils.image_dataset_from_directory(
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"test_data_own_skeleton",
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size
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)
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class_names = train_ds.class_names
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print("Class names:",class_names)
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print("Total classes:",len(class_names))
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from tensorflow.keras import Sequential
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from tensorflow.keras import layers
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
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from keras import models, layers
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from tensorflow.keras.applications import VGG19
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img_size = [img_height, img_width]
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vgg_model = VGG19(input_shape=img_size + [3], weights='imagenet', include_top=False)
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x = layers.Flatten()(vgg_model.output)
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x = layers.Dense(len(class_names), activation='softmax')(x)
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model = models.Model(vgg_model.input, x)
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# for layer in model.layers[:-1]:
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# layer.trainable = False
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model.compile(loss='sparse_categorical_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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from tensorflow.keras.callbacks import EarlyStopping
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early_stopping = EarlyStopping(
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monitor="val_accuracy", patience=12,
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restore_best_weights=True,
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)
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history = model.fit(
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train_ds,
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batch_size=128,
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validation_data=test_ds,
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validation_batch_size=128,
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epochs=50,
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callbacks=[early_stopping]
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)
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model.save('VGG19_sign_char_detection_model')
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