# In[ ]:
base_pretrained_model = PTModel(input_shape=t_x.shape[1:],
include_top=False,
weights='imagenet')
base_pretrained_model.trainable = False
# In[ ]:
from keras import models, layers
from keras.optimizers import Adam
img_in = layers.Input(t_x.shape[1:], name='Image_RGB_In')
img_noise = layers.GaussianNoise(GAUSSIAN_NOISE)(img_in)
pt_features = base_pretrained_model(img_noise)
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
bn_features = layers.BatchNormalization()(pt_features)
feature_dropout = layers.SpatialDropout2D(DROPOUT)(bn_features)
gmp_dr = layers.GlobalMaxPooling2D()(feature_dropout)
dr_steps = layers.Dropout(DROPOUT)(layers.Dense(DENSE_COUNT,
activation='relu')(gmp_dr))
out_layer = layers.Dense(1, activation='sigmoid')(dr_steps)
ship_model = models.Model(inputs=[img_in],
outputs=[out_layer],
name='full_model')
ship_model.compile(optimizer=Adam(lr=LEARN_RATE),
loss='binary_crossentropy',
metrics=['binary_accuracy'])
base_pretrained_model = PTModel(input_shape = t_x.shape[1:],
include_top = False, weights = 'imagenet')
base_pretrained_model.trainable = False
# ## Model Supplements
# Here we add a few other layers to the model to make it better suited for the classification problem.
# In[ ]:
from keras.layers import GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, Conv2D, multiply, LocallyConnected2D, Lambda, AvgPool2D
from keras.models import Model
from keras.optimizers import Adam
pt_features = Input(base_pretrained_model.get_output_shape_at(0)[1:], name = 'feature_input')
pt_depth = base_pretrained_model.get_output_shape_at(0)[-1]
from keras.layers import BatchNormalization
bn_features = BatchNormalization()(pt_features)
gap = GlobalAveragePooling2D()(bn_features)
gap_dr = Dropout(DROPOUT)(gap)
dr_steps = Dropout(DROPOUT)(Dense(DENSE_COUNT, activation = 'elu')(gap_dr))
out_layer = Dense(t_y.shape[1], activation = 'softmax')(dr_steps)
attn_model = Model(inputs = [pt_features],
outputs = [out_layer], name = 'trained_model')
attn_model.summary()
