def SqueezeNet(include_top=True, weights='<STR_LIT>',<EOL>input_tensor=None, input_shape=None,<EOL>pooling=None,<EOL>classes=<NUM_LIT:1000>):
if weights not in {'<STR_LIT>', None}:<EOL><INDENT>raise ValueError('<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>')<EOL><DEDENT>if weights == '<STR_LIT>' and classes != <NUM_LIT:1000>:<EOL><INDENT>raise ValueError('<STR_LIT>'<EOL>'<STR_LIT>')<EOL><DEDENT>input_shape = _obtain_input_shape(input_shape,<EOL>default_size=<NUM_LIT>,<EOL>min_size=<NUM_LIT>,<EOL>data_format=K.image_data_format(),<EOL>require_flatten=include_top)<EOL>if input_tensor is None:<EOL><INDENT>img_input = Input(shape=input_shape)<EOL><DEDENT>else:<EOL><INDENT>if not K.is_keras_tensor(input_tensor):<EOL><INDENT>img_input = Input(tensor=input_tensor, shape=input_shape)<EOL><DEDENT>else:<EOL><INDENT>img_input = input_tensor<EOL><DEDENT><DEDENT>x = Convolution2D(<NUM_LIT:64>, (<NUM_LIT:3>, <NUM_LIT:3>), strides=(<NUM_LIT:2>, <NUM_LIT:2>), padding='<STR_LIT>', name='<STR_LIT>')(img_input)<EOL>x = Activation('<STR_LIT:relu>', name='<STR_LIT>')(x)<EOL>x = MaxPooling2D(pool_size=(<NUM_LIT:3>, <NUM_LIT:3>), strides=(<NUM_LIT:2>, <NUM_LIT:2>), name='<STR_LIT>')(x)<EOL>x = fire_module(x, fire_id=<NUM_LIT:2>, squeeze=<NUM_LIT:16>, expand=<NUM_LIT:64>)<EOL>x = fire_module(x, fire_id=<NUM_LIT:3>, squeeze=<NUM_LIT:16>, expand=<NUM_LIT:64>)<EOL>x = MaxPooling2D(pool_size=(<NUM_LIT:3>, <NUM_LIT:3>), strides=(<NUM_LIT:2>, <NUM_LIT:2>), name='<STR_LIT>')(x)<EOL>x = fire_module(x, fire_id=<NUM_LIT:4>, squeeze=<NUM_LIT:32>, expand=<NUM_LIT>)<EOL>x = fire_module(x, fire_id=<NUM_LIT:5>, squeeze=<NUM_LIT:32>, expand=<NUM_LIT>)<EOL>x = MaxPooling2D(pool_size=(<NUM_LIT:3>, <NUM_LIT:3>), strides=(<NUM_LIT:2>, <NUM_LIT:2>), name='<STR_LIT>')(x)<EOL>x = fire_module(x, fire_id=<NUM_LIT:6>, squeeze=<NUM_LIT>, expand=<NUM_LIT>)<EOL>x = fire_module(x, fire_id=<NUM_LIT:7>, squeeze=<NUM_LIT>, expand=<NUM_LIT>)<EOL>x = fire_module(x, fire_id=<NUM_LIT:8>, squeeze=<NUM_LIT:64>, expand=<NUM_LIT>)<EOL>x = fire_module(x, fire_id=<NUM_LIT:9>, squeeze=<NUM_LIT:64>, expand=<NUM_LIT>)<EOL>if include_top:<EOL><INDENT>x = Dropout(<NUM_LIT:0.5>, name='<STR_LIT>')(x)<EOL>x = Convolution2D(classes, (<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', name='<STR_LIT>')(x)<EOL>x = Activation('<STR_LIT:relu>', name='<STR_LIT>')(x)<EOL>x = GlobalAveragePooling2D()(x)<EOL>x = Activation('<STR_LIT>', name='<STR_LIT>')(x)<EOL><DEDENT>else:<EOL><INDENT>if pooling == '<STR_LIT>':<EOL><INDENT>x = GlobalAveragePooling2D()(x)<EOL><DEDENT>elif pooling=='<STR_LIT>':<EOL><INDENT>x = GlobalMaxPooling2D()(x)<EOL><DEDENT>elif pooling==None:<EOL><INDENT>pass<EOL><DEDENT>else:<EOL><INDENT>raise ValueError("<STR_LIT>" + pooling)<EOL><DEDENT><DEDENT>if input_tensor is not None:<EOL><INDENT>inputs = get_source_inputs(input_tensor)<EOL><DEDENT>else:<EOL><INDENT>inputs = img_input<EOL><DEDENT>model = Model(inputs, x, name='<STR_LIT>')<EOL>if weights == '<STR_LIT>':<EOL><INDENT>if include_top:<EOL><INDENT>weights_path = get_file('<STR_LIT>',<EOL>WEIGHTS_PATH,<EOL>cache_subdir='<STR_LIT>')<EOL><DEDENT>else:<EOL><INDENT>weights_path = get_file('<STR_LIT>',<EOL>WEIGHTS_PATH_NO_TOP,<EOL>cache_subdir='<STR_LIT>')<EOL><DEDENT>model.load_weights(weights_path)<EOL>if K.backend() == '<STR_LIT>':<EOL><INDENT>layer_utils.convert_all_kernels_in_model(model)<EOL><DEDENT>if K.image_data_format() == '<STR_LIT>':<EOL><INDENT>if K.backend() == '<STR_LIT>':<EOL><INDENT>warnings.warn('<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>'<EOL>'<STR_LIT>')<EOL><DEDENT><DEDENT><DEDENT>return model<EOL>
Instantiates the SqueezeNet architecture.