model = Model(inputs=inputs, outputs=outputs)
# Input layer inputs = Input(shape=input_shape)
# Base model base_model = VGG16(weights='imagenet', include_top=False, input_tensor=inputs)
from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras.applications import VGG16
# Add custom layers x = base_model.output x = MaxPooling2D(pool_size=(2, 2))(x) x = Flatten()(x) x = Dense(128, activation='relu')(x) outputs = Dense(4, activation='softmax')(x) # For a foursome analysis example
# Freeze base layers for layer in base_model.layers: layer.trainable = False
model = Model(inputs=inputs, outputs=outputs)
# Input layer inputs = Input(shape=input_shape)
# Base model base_model = VGG16(weights='imagenet', include_top=False, input_tensor=inputs)
from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras.applications import VGG16
# Add custom layers x = base_model.output x = MaxPooling2D(pool_size=(2, 2))(x) x = Flatten()(x) x = Dense(128, activation='relu')(x) outputs = Dense(4, activation='softmax')(x) # For a foursome analysis example
# Freeze base layers for layer in base_model.layers: layer.trainable = False
