我们从Python开源项目中，提取了以下36个代码示例，用于说明如何使用keras.layers.MaxPooling3D()。
def create_model_2(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_1(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) x = Flatten()(pool1) x = Dense(64, init='normal')(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=1e-5) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def unet_model_xd3_2_6l_grid(nb_filter=48, dim=5, clen=3 , img_rows=224, img_cols=224 ): # NOTE that this procedure is/should be used with img_rows & img_cols as None # aiming for architecture similar to the http://cs231n.stanford.edu/reports2016/317_Report.pdf # Our model is six layers deep, consisting of a series of three CONV-RELU-POOL layyers (with 32, 32, and 64 3x3 filters), a CONV-RELU layer (with 128 3x3 filters), three UPSCALE-CONV-RELU lay- ers (with 64, 32, and 32 3x3 filters), and a final 1x1 CONV- SIGMOID layer to output pixel-level predictions. Its struc- ture resembles Figure 2, though with the number of pixels, filters, and levels as described here ## 3D CNN version of a previously developed unet_model_xd_6j zconv = clen inputs = Input((1, dim, img_rows, img_cols)) conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(inputs) conv1 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1) conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool1) conv2 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2) conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(pool2) conv4 = Convolution3D(4*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv4) up6 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv2], mode='concat', concat_axis=1) conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up6) conv6 = Convolution3D(2*nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv6) up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1) # original - only works for even dim conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(up7) conv7 = Convolution3D(nb_filter, zconv, clen, clen, activation='relu', border_mode='same')(conv7) pool11 = MaxPooling3D(pool_size=(2, 1, 1))(conv7) conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool11) conv12 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv12) pool12 = MaxPooling3D(pool_size=(2, 1, 1))(conv12) conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool12) conv13 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv13) pool13 = MaxPooling3D(pool_size=(2, 1, 1))(conv13) if (dim < 16): conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool13) else: # need one extra layer to get to 1D x 2D mask ... conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(pool13) conv14 = Convolution3D(2*nb_filter, zconv, 1, 1, activation='relu', border_mode='same')(conv14) pool14 = MaxPooling3D(pool_size=(2, 1, 1))(conv14) conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(pool14) model = Model(input=inputs, output=conv8) model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef]) #model.compile(optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), loss=dice_coef_loss, metrics=[dice_coef]) return model
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model: inputs = Input(shape=input_shape, name="input_1") x = inputs #x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x) x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x) # 2nd layer group x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x) #if USE_DROPOUT: # x = Dropout(p=0.3)(x) # 3rd layer group x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x) x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x) #if USE_DROPOUT: # x = Dropout(p=0.4)(x) # 4th layer group x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x) x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x) #if USE_DROPOUT: # x = Dropout(p=0.5)(x) last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x) out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64) out_class = Flatten(name="out_class")(out_class) out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64) out_malignancy = Flatten(name="out_malignancy")(out_malignancy) model = Model(input=inputs, output=[out_class, out_malignancy]) if load_weight_path is not None: model.load_weights(load_weight_path, by_name=False) #model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error}) model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy"}, metrics={"out_class": [binary_accuracy, binary_crossentropy]}) if features: model = Model(input=inputs, output=[last64]) model.summary(line_length=140) return model
def test_keras_import(self): # Global Pooling 1D model = Sequential() model.add(GlobalMaxPooling1D(input_shape=(1, 16))) model.build() self.keras_param_test(model, 0, 5) # Global Pooling 2D model = Sequential() model.add(GlobalMaxPooling2D(input_shape=(1, 16, 16))) model.build() self.keras_param_test(model, 0, 8) # Pooling 1D model = Sequential() model.add(MaxPooling1D(pool_size=2, strides=2, padding='same', input_shape=(1, 16))) model.build() self.keras_param_test(model, 0, 5) # Pooling 2D model = Sequential() model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', input_shape=(1, 16, 16))) model.build() self.keras_param_test(model, 0, 8) # Pooling 3D model = Sequential() model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same', input_shape=(1, 16, 16, 16))) model.build() self.keras_param_test(model, 0, 11) # ********** Locally-connected Layers **********
def test_keras_export(self): tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app', 'keras_export_test.json'), 'r') response = json.load(tests) tests.close() net = yaml.safe_load(json.dumps(response['net'])) net = {'l0': net['Input'], 'l1': net['Input2'], 'l2': net['Input4'], 'l3': net['Pooling']} # Pool 1D net['l1']['connection']['output'].append('l3') net['l3']['connection']['input'] = ['l1'] net['l3']['params']['layer_type'] = '1D' net['l3']['shape']['input'] = net['l1']['shape']['output'] net['l3']['shape']['output'] = [12, 12] inp = data(net['l1'], '', 'l1')['l1'] temp = pooling(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[2].__class__.__name__, 'MaxPooling1D') # Pool 2D net['l0']['connection']['output'].append('l0') net['l3']['connection']['input'] = ['l0'] net['l3']['params']['layer_type'] = '2D' net['l3']['shape']['input'] = net['l0']['shape']['output'] net['l3']['shape']['output'] = [3, 226, 226] inp = data(net['l0'], '', 'l0')['l0'] temp = pooling(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[2].__class__.__name__, 'MaxPooling2D') # Pool 3D net['l2']['connection']['output'].append('l3') net['l3']['connection']['input'] = ['l2'] net['l3']['params']['layer_type'] = '3D' net['l3']['shape']['input'] = net['l2']['shape']['output'] net['l3']['shape']['output'] = [3, 226, 226, 18] inp = data(net['l2'], '', 'l2')['l2'] temp = pooling(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[2].__class__.__name__, 'MaxPooling3D') # ********** Locally-connected Layers **********
def create_model_3_noise2(): inputs = Input((32, 32, 32, 1)) noise = GaussianNoise(sigma=0.02)(inputs) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise) conv1 = SpatialDropout3D(0.4)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.4)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(128, init='normal')(x) x = Dropout(0.5)(x) x = Dense(64, init='normal')(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_3_noise(): inputs = Input((32, 32, 32, 1)) noise = GaussianNoise(sigma=0.05)(inputs) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(noise) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.000001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_8(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.2)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) conv1 = SpatialDropout3D(0.2)(conv1) conv1 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.2)(conv2) conv2 = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_7(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 5, 5, 5, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 5, 5, 5, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 5, 5, 5, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_6(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_4(): inputs1 = Input((32, 32, 32, 1)) inputs2 = Input((6,)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs1) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = merge([x, inputs2], mode='concat') x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=[inputs1,inputs2], output=predictions) model.summary() optimizer = Adam() model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def create_model_3(): inputs = Input((32, 32, 32, 1)) #noise = GaussianNoise(sigma=0.1)(x) conv1 = Convolution3D(32, 3, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = SpatialDropout3D(0.1)(conv1) conv1 = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling3D(pool_size=(2,2, 2))(conv1) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = SpatialDropout3D(0.1)(conv2) conv2 = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling3D(pool_size=(2,2, 2))(conv2) x = Flatten()(pool2) x = Dense(64, init='normal')(x) x = Dropout(0.5)(x) predictions = Dense(1, init='normal', activation='sigmoid')(x) model = Model(input=inputs, output=predictions) model.summary() optimizer = Adam(lr=0.00001) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['binary_accuracy','precision','recall','mean_squared_error','accuracy']) return model
def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model: inputs = Input(shape=input_shape, name="input_1") x = inputs x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x) x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x) # 2nd layer group x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x) if USE_DROPOUT: x = Dropout(p=0.3)(x) # 3rd layer group x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x) x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x) if USE_DROPOUT: x = Dropout(p=0.4)(x) # 4th layer group x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x) x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x) x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x) if USE_DROPOUT: x = Dropout(p=0.5)(x) last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x) out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64) out_class = Flatten(name="out_class")(out_class) out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64) out_malignancy = Flatten(name="out_malignancy")(out_malignancy) model = Model(input=inputs, output=[out_class, out_malignancy]) if load_weight_path is not None: model.load_weights(load_weight_path, by_name=False) model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error}) if features: model = Model(input=inputs, output=[last64]) model.summary(line_length=140) return model
def load_model(): # use simple CNN structure in_shape = (SequenceLength, IMSIZE[0], IMSIZE[1], 3) model = Sequential() model.add(ConvLSTM2D(32, kernel_size=(7, 7), padding='valid', return_sequences=True, input_shape=in_shape)) model.add(Activation('relu')) model.add(MaxPooling3D(pool_size=(1, 2, 2))) model.add(ConvLSTM2D(64, kernel_size=(5, 5), padding='valid', return_sequences=True)) model.add(MaxPooling3D(pool_size=(1, 2, 2))) model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True)) model.add(Activation('relu')) model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True)) model.add(Activation('relu')) model.add(ConvLSTM2D(96, kernel_size=(3, 3), padding='valid', return_sequences=True)) model.add(MaxPooling3D(pool_size=(1, 2, 2))) model.add(Dense(320)) model.add(Activation('relu')) model.add(Dropout(0.5)) out_shape = model.output_shape # print('====Model shape: ', out_shape) model.add(Reshape((SequenceLength, out_shape[2] * out_shape[3] * out_shape[4]))) model.add(LSTM(64, return_sequences=False)) model.add(Dropout(0.5)) model.add(Dense(N_CLASSES, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) # model structure summary print(model.summary()) return model
def build(video_shape, audio_spectrogram_size): model = Sequential() model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero1', input_shape=video_shape)) model.add(Convolution3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 2, 2), name='zero2')) model.add(Convolution3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2')) model.add(Dropout(0.25)) model.add(ZeroPadding3D(padding=(1, 1, 1), name='zero3')) model.add(Convolution3D(128, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3')) model.add(Dropout(0.25)) model.add(TimeDistributed(Flatten(), name='time')) model.add(Dense(1024, kernel_initializer='he_normal', name='dense1')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(1024, kernel_initializer='he_normal', name='dense2')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(2048, kernel_initializer='he_normal', name='dense3')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(2048, kernel_initializer='he_normal', name='dense4')) model.add(BatchNormalization()) model.add(LeakyReLU()) model.add(Dropout(0.25)) model.add(Dense(audio_spectrogram_size, name='output')) model.summary() return VideoToSpeechNet(model)
def ecog_3d_model(channels=None, weights=None): input_tensor = Input(shape=(1,8,8, 1000)) # Block 1 x = AveragePooling3D((1, 1, 5), name='pre_pool')(input_tensor) x = Convolution3D(4, 2, 2, 3, border_mode='same', name='block1_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((2, 2, 3), name='block1_pool')(x) # Block 2 x = Convolution3D(8, 2, 2, 3, border_mode='same', name='block2_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D(( 1, 1, 3), name='block2_pool')(x) # Block 3 x = Convolution3D(16, 2,2, 3, border_mode='same', name='block3_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((1, 1, 2), name='block3_pool')(x) # Block 4 # x = Convolution2D(32, 1, 3, border_mode='same', name='block4_conv1')(x) # x = BatchNormalization(axis=1)(x) # x = Activation('relu')(x) # x = MaxPooling2D((1, 2), name='block4_pool')(x) x = Flatten(name='flatten')(x) x = Dropout(0.5)(x) x = Dense(64, W_regularizer=l2(0.01), name='fc1')(x) #x = BatchNormalization()(x) #x = Activation('relu')(x) #x = Dropout(0.5)(x) #x = Dense(1, name='predictions')(x) # x = BatchNormalization()(x) predictions = Activation('sigmoid')(x) # for layer in base_model.layers[:10]: # layer.trainable = False model = Model(input=input_tensor, output=predictions) if weights is not None: model.load_weights(weights) return model
def pooling(layer, layer_in, layerId): poolMap = { ('1D', 'MAX'): MaxPooling1D, ('2D', 'MAX'): MaxPooling2D, ('3D', 'MAX'): MaxPooling3D, ('1D', 'AVE'): AveragePooling1D, ('2D', 'AVE'): AveragePooling2D, ('3D', 'AVE'): AveragePooling3D, } out = {} layer_type = layer['params']['layer_type'] pool_type = layer['params']['pool'] padding = get_padding(layer) if (layer_type == '1D'): strides = layer['params']['stride_w'] kernel = layer['params']['kernel_w'] if (padding == 'custom'): p_w = layer['params']['pad_w'] out[layerId + 'Pad'] = ZeroPadding1D(padding=p_w)(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] elif (layer_type == '2D'): strides = (layer['params']['stride_h'], layer['params']['stride_w']) kernel = (layer['params']['kernel_h'], layer['params']['kernel_w']) if (padding == 'custom'): p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w'] out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] else: strides = (layer['params']['stride_h'], layer['params']['stride_w'], layer['params']['stride_d']) kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'], layer['params']['kernel_d']) if (padding == 'custom'): p_h, p_w, p_d = layer['params']['pad_h'], layer['params']['pad_w'],\ layer['params']['pad_d'] out[layerId + 'Pad'] = ZeroPadding3D(padding=(p_h, p_w, p_d))(*layer_in) padding = 'valid' layer_in = [out[layerId + 'Pad']] out[layerId] = poolMap[(layer_type, pool_type)](pool_size=kernel, strides=strides, padding=padding)( *layer_in) return out # ********** Locally-connected Layers **********
