mirror of
https://github.com/tengge1/ShadowEditor.git
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152 lines
5.2 KiB
Python
152 lines
5.2 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import tensorflow as tf
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from tensorflow import keras
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import numpy as np
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import matplotlib.pyplot as plt
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print(tf.__version__)
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NUM_WORDS = 10000
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(train_data, train_labels), (test_data,
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test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)
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def multi_hot_sequences(sequences, dimension):
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# Create an all-zero matrix of shape (len(sequences), dimension)
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results = np.zeros((len(sequences), dimension))
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for i, word_indices in enumerate(sequences):
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# set specific indices of results[i] to 1s
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results[i, word_indices] = 1.0
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return results
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train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)
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test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)
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plt.plot(train_data[0])
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baseline_model = keras.Sequential([
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# `input_shape` is only required here so that `.summary` works.
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keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),
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keras.layers.Dense(16, activation='relu'),
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keras.layers.Dense(1, activation='sigmoid')
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])
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baseline_model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy', 'binary_crossentropy'])
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baseline_model.summary()
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baseline_history = baseline_model.fit(train_data,
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train_labels,
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epochs=20,
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batch_size=512,
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validation_data=(test_data, test_labels),
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verbose=2)
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smaller_model = keras.Sequential([
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keras.layers.Dense(4, activation='relu', input_shape=(NUM_WORDS,)),
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keras.layers.Dense(4, activation='relu'),
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keras.layers.Dense(1, activation='sigmoid')
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])
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smaller_model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy', 'binary_crossentropy'])
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smaller_model.summary()
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smaller_history = smaller_model.fit(train_data,
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train_labels,
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epochs=20,
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batch_size=512,
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validation_data=(test_data, test_labels),
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verbose=2)
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bigger_model = keras.models.Sequential([
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keras.layers.Dense(512, activation='relu', input_shape=(NUM_WORDS,)),
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keras.layers.Dense(512, activation='relu'),
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keras.layers.Dense(1, activation='sigmoid')
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])
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bigger_model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy', 'binary_crossentropy'])
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bigger_model.summary()
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bigger_history = bigger_model.fit(train_data, train_labels,
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epochs=20,
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batch_size=512,
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validation_data=(test_data, test_labels),
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verbose=2)
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def plot_history(histories, key='binary_crossentropy'):
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plt.figure(figsize=(16, 10))
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for name, history in histories:
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val = plt.plot(history.epoch, history.history['val_'+key],
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'--', label=name.title()+' Val')
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plt.plot(history.epoch, history.history[key], color=val[0].get_color(),
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label=name.title()+' Train')
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plt.xlabel('Epochs')
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plt.ylabel(key.replace('_', ' ').title())
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plt.legend()
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plt.xlim([0, max(history.epoch)])
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plot_history([('baseline', baseline_history),
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('smaller', smaller_history),
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('bigger', bigger_history)])
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l2_model = keras.models.Sequential([
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keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
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activation='relu', input_shape=(NUM_WORDS,)),
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keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
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activation='relu'),
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keras.layers.Dense(1, activation='sigmoid')
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])
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l2_model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy', 'binary_crossentropy'])
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l2_model_history = l2_model.fit(train_data, train_labels,
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epochs=20,
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batch_size=512,
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validation_data=(test_data, test_labels),
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verbose=2)
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plot_history([('baseline', baseline_history),
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('l2', l2_model_history)])
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dpt_model = keras.models.Sequential([
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keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),
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keras.layers.Dropout(0.5),
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keras.layers.Dense(16, activation='relu'),
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keras.layers.Dropout(0.5),
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keras.layers.Dense(1, activation='sigmoid')
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])
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dpt_model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy', 'binary_crossentropy'])
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dpt_model_history = dpt_model.fit(train_data, train_labels,
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epochs=20,
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batch_size=512,
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validation_data=(test_data, test_labels),
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verbose=2)
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plot_history([('baseline', baseline_history),
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('dropout', dpt_model_history)])
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