Train a model for IMDB sentiment classification. Args: model_type: Type of the model to train, as a `str`. vocabulary_size: Vocabulary size. embedding_size: Embedding dimensions. x_train: An int array of shape `(num_examples, max_len)`: index-encoded sentences. y_train:
(model_type,
vocabulary_size,
embedding_size,
x_train,
y_train,
x_test,
y_test,
epochs,
batch_size)
| 93 | |
| 94 | |
| 95 | def train_model(model_type, |
| 96 | vocabulary_size, |
| 97 | embedding_size, |
| 98 | x_train, |
| 99 | y_train, |
| 100 | x_test, |
| 101 | y_test, |
| 102 | epochs, |
| 103 | batch_size): |
| 104 | """Train a model for IMDB sentiment classification. |
| 105 | |
| 106 | Args: |
| 107 | model_type: Type of the model to train, as a `str`. |
| 108 | vocabulary_size: Vocabulary size. |
| 109 | embedding_size: Embedding dimensions. |
| 110 | x_train: An int array of shape `(num_examples, max_len)`: index-encoded |
| 111 | sentences. |
| 112 | y_train: An int array of shape `(num_examples,)`: labels for the sentences. |
| 113 | x_test: Same as `x_train`, but for test. |
| 114 | y_test: Same as `y_train`, but for test. |
| 115 | epochs: Number of epochs to train the model for. |
| 116 | batch_size: Batch size to use during trainng. |
| 117 | |
| 118 | Returns: |
| 119 | The trained model instance. |
| 120 | |
| 121 | Raises: |
| 122 | ValueError: on invalid model type. |
| 123 | """ |
| 124 | |
| 125 | model = tf.keras.Sequential() |
| 126 | model.add(tf.keras.layers.Embedding(vocabulary_size, embedding_size)) |
| 127 | if model_type == 'bidirectional_lstm': |
| 128 | # TODO(cais): Uncomment the following once bug b/74429960 is fixed. |
| 129 | # model.add(tf.keras.layers.Embedding( |
| 130 | # vocabulary_size, 128, input_length=maxlen)) |
| 131 | # model.add(tf.keras.layers.Bidirectional( |
| 132 | # tf.keras.layers.LSTM(64)) |
| 133 | # model.add(tf.keras.layers.Dropout(0.5)) |
| 134 | raise NotImplementedError() |
| 135 | elif model_type == 'cnn': |
| 136 | model.add(tf.keras.layers.Dropout(0.2)) |
| 137 | model.add(tf.keras.layers.Conv1D(250, |
| 138 | 3, |
| 139 | padding='valid', |
| 140 | activation='relu', |
| 141 | strides=1)) |
| 142 | model.add(tf.keras.layers.GlobalMaxPooling1D()) |
| 143 | model.add(tf.keras.layers.Dense(250, activation='relu')) |
| 144 | elif model_type == 'lstm': |
| 145 | model.add(tf.keras.layers.LSTM(128)) |
| 146 | else: |
| 147 | raise ValueError("Invalid model type: '%s'" % model_type) |
| 148 | model.add(tf.keras.layers.Dense(1, activation='sigmoid')) |
| 149 | |
| 150 | model.compile('adam', 'binary_crossentropy', metrics=['accuracy']) |
| 151 | model.fit(x_train, y_train, |
| 152 | batch_size=batch_size, |