add info for net.py
Esse commit está contido em:
Charlie Hewitt
+17
-93
@@ -125,59 +125,6 @@ def mobilenet_style_model(C_or_R, dropout=0):
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return model
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def hybrid_style_model(C_or_R, dropout=(0.2, 0.5)):
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model = Sequential()
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# INPUT BLOCK
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model.add(Conv2D(16, (3, 3), input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), strides=(2, 2), padding='same', use_bias=False))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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# CONV BLOCK 1
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model = depth_conv_block(model, 32, 3, 1)
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model = depth_conv_block(model, 32, 3, 1)
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
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model.add(GaussianDropout(dropout[0]))
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# CONV BLOCK 2
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model = depth_conv_block(model, 64, 3, 1)
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model = depth_conv_block(model, 64, 3, 1)
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
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model.add(GaussianDropout(dropout[0]))
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# CONV BLOCK 3
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model = depth_conv_block(model, 128, 3, 1)
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model = depth_conv_block(model, 128, 3, 1)
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
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model.add(GaussianDropout(dropout[0]))
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# CONV BLOCK 4
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model = depth_conv_block(model, 256, 3, 1)
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model = depth_conv_block(model, 256, 3, 1)
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
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model.add(GaussianDropout(dropout[0]))
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# CONV BLOCK 4
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model = depth_conv_block(model, 512, 3, 1)
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model = depth_conv_block(model, 512, 3, 1)
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model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
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model.add(GaussianDropout(dropout[0]))
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# FLATTEN
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model.add(Flatten())
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# DENSE 1
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model.add(Dense(1024, use_bias=False))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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model.add(Dropout(dropout[1]))
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# DENSE 1
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model.add(Dense(1024, use_bias=False))
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model.add(BatchNormalization())
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model.add(Activation('relu'))
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model.add(Dropout(dropout[1]))
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# OUTPUT
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if C_or_R == CLASSIFY:
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model.add(Dense(8, activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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else:
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model.add(Dense(2, activation='linear'))
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model.compile(loss='mean_squared_error', optimizer='adam')
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return model
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def vgg_style_model(C_or_R, dropout=(0.2, 0.5)):
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model = Sequential()
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# CONV BLOCK 1
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@@ -315,18 +262,18 @@ def regressor_from_classifier(model, drop=False):
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def load_and_save(model, m):
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model.load_weights(m + '/R_T.h5')
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model.load_weights(m + '.h5')
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for layer in model.layers:
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if type(layer) is Dropout or type(layer) is GaussianDropout:
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model.layers.remove(layer)
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model.save(m + '/R_OUT.h5')
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model.save(m + '_out.h5')
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def visualise(model, name):
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plot_model(model, to_file=name + '.png', show_shapes=True, show_layer_names=False)
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def train(C_or_R, model, name, epochs, batch_size, test=False):
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def train(C_or_R, model, output_path, epochs, batch_size):
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print('** LOADING DATA **')
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t_paths = np.load('training_paths.npy')
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t_labels = np.load('training_labels.npy')
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@@ -335,12 +282,8 @@ def train(C_or_R, model, name, epochs, batch_size, test=False):
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v_labels = np.load('validation_labels.npy')
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v_paths, v_labels, v_weights = process_data(C_or_R, v_paths, v_labels)
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print('** FITTING MODEL **')
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if test:
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t_steps = 3
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v_steps = 3
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else:
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t_steps = len(t_labels) // batch_size
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v_steps = len(v_labels) // batch_size
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t_steps = len(t_labels) // batch_size
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v_steps = len(v_labels) // batch_size
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if C_or_R == CLASSIFY:
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history = model.fit_generator(
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load_images(C_or_R, t_paths, t_labels, batch_size),
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@@ -349,7 +292,7 @@ def train(C_or_R, model, name, epochs, batch_size, test=False):
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epochs=epochs,
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validation_data=load_images(C_or_R, v_paths, v_labels, batch_size, eval=True),
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validation_steps=v_steps,
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callbacks=[ModelCheckpoint(name + '_T.h5', monitor='val_acc', save_best_only=True)])
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callbacks=[ModelCheckpoint(output_path + '_T.h5', monitor='val_acc', save_best_only=True)])
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else:
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history = model.fit_generator(
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load_images(C_or_R, t_paths, t_labels, batch_size),
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@@ -357,42 +300,23 @@ def train(C_or_R, model, name, epochs, batch_size, test=False):
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epochs=epochs,
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validation_data=load_images(C_or_R, v_paths, v_labels, batch_size, eval=True),
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validation_steps=v_steps,
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callbacks=[ModelCheckpoint(name + '_T.h5', save_best_only=True)])
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callbacks=[ModelCheckpoint(output_path + '_T.h5', save_best_only=True)])
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print('** EXPORTING MODEL **')
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np.save(name + '_HIST', history.history)
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np.save(output_path + '_HIST', history.history)
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for layer in model.layers:
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if type(layer) is Dropout or type(layer) is GaussianDropout:
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model.layers.remove(layer)
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model_json = model.to_json()
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with open(name + '_ARCH.json', 'w') as json_file:
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with open(output_path + '_ARCH.json', 'w') as json_file:
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json_file.write(model_json)
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model.save_weights(name + '_WEIGHTS.h5')
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model.save(name + '_FULL.h5')
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model.save_weights(output_path + '_WEIGHTS.h5')
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model.save(output_path + '_FULL.h5')
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if __name__ == '__main__':
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# model = load_model('M_MOB/C_T.h5', custom_objects={'DepthwiseConv2D': DepthwiseConv2D})
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# weights = model.layers[-1].get_weights()
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# model.layers.pop()
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# model.outputs = [model.layers[-1].output]
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# model.layers[-1].outbound_nodes = []
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# model.add(Dropout(0.3))
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# model.add(Dense(8, activation='softmax'))
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# model.layers[-1].set_weights(weights)
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# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# train(CLASSIFY, model, 'M_MOB/C_2', 12, 250)
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#
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# model = vgg_style_model(CLASSIFY, dropout=(0.1, 0.3))
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# model.load_weights('M_VGG/C_T.h5')
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# train(CLASSIFY, model, 'M_VGG/C_3', 12, 400)
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#
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model = vgg_style_model(REGRESS, (0, 0))
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model.load_weights('M_VGG/R_T.h5')
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load_and_save(model, 'M_VGG')
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exit()
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model = alexnet_style_model(CLASSIFY, dropout=(0.1, 0.3))
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model.load_weights('M_ALEX/C_T.h5')
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train(CLASSIFY, model, 'M_ALEX/C_3', 12, 400)
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model = load_model('M_VGG/C_OUT.h5')
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model = regressor_from_classifier(model)
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train(REGRESS, model, 'M_VGG/R_2', 16, 400)
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print('Haven\'t got around to a CLI for this')
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print('Choose a model from mobilenet_style_model, vgg_style_model, alexnet_style_model')
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print('C_or_R means classifier or regressor')
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print('Call train() on the model with appropriate C_or_R, output and train options')
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print('A pretrained classifier can be loaded and passed to regressor_from_classifier() to get a regression model which can be trained as normal with C_or_R=REGRESS')
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print('load_and_save() is used to trim unneeded data from a model to get the true output model, give it a fresh initialization of the right mdoel type and path to the trained model')
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