የ Keras እና የኮድ ጥቂት መስመሮች ጋር እንዴት አንድ መሐንዲት ለመፍጠር

አጠቃላይ ግምገማ አግኙን አግኙን ስልጠና, ግምገማ እና ግምገማ ያግኙ እና serialize ተመሳሳይ ግራፊዎችን በመጠቀም ከሌሎች ሞዴሎች ለመፍጠር ሁሉም ሞዴሎች a callable, እንደ layers የኮምፒውተር የኮምፒውተር የኮምፒውተር ብዙ input እና outputs ጋር ሞዴሎች አንድ ተስማሚ ሞዴል አግኙን import numpy as np import tensorflow as tf from tensorflow import keras from keras import layers አግኙን ካርታዎች ይህ ሞዴሎች ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ነው. የ functional API በ non-linear topology, shared layers, and even multiple inputs or outputs ጋር ሞዴሎች መጠቀም ይችላሉ. አጠቃቀም API keras.Sequential የ Deep Learning ሞዴል አብዛኛውን ጊዜ የካይሲክ ግራፍ (DAG) ነው. . ግራፊዎች ቀጣይ ሞዴል ይመልከቱ: [Dense (64 ዩኒት, relu activation)] [Dense (64 ዩኒት, relu activation]] [Dense (10 ዩኒት, softmax activation]]] (የተግበሪያ: በ 10 ደረጃዎች ላይ አንድ ትክክለኛነት ቅርጸት logits) ይህ የ 3 ክፍሎች ጋር አንድ ዋና ግራፊክ ነው. ይህ ሞዴል በመጠቀም ተግባራዊ API በመጠቀም ለመፍጠር, አንድ input node ለመፍጠር ይጀምራል: inputs = keras.Input(shape=(784,)) ውሂብ ቅርጸት በ 784 ዲሜትር ቪክቶር እንደ ይመዝገቡ. የ batch መጠን ሁልጊዜ ይመዝገቡ ምክንያቱም ብቻ እያንዳንዱ ሞዴል ቅርጸት ይመዝገቡ. ለምሳሌ, እርስዎ አንድ ቅርጸት ጋር አንድ ፎቶ መውሰድ ከሆነ እርስዎ ይጠቀማል: (32, 32, 3) # Just for demonstration purposes. img_inputs = keras.Input(shape=(32, 32, 3)) The ያግኙን ያግኙን ያግኙን መረጃ እና የእርስዎን ሞዴል ላይ ያግኙን ውሂብ ውሂብ. እዚህ ቅርጽ ነው: inputs dtype inputs.shape TensorShape([None, 784]) እዚህ የ Dtype ነው: inputs.dtype tf.float32 አንተ ይህ ግራፍ ላይ አንድ ግራፍ ይደውሉ.You create a new node in the graph of layers by calling a layer on this object: inputs dense = layers.Dense(64, activation="relu") x = dense(inputs) የ "Layer Call" ተግባር ከ "Inputs" ወደ አንተ የተቋቋመ የ "Layer Call" ተግባር ተመሳሳይ ነው. layer, and you get ምርት እንደ. dense x ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ ከባድ x = layers.Dense(64, activation="relu")(x) outputs = layers.Dense(10)(x) በዚህ ጊዜ, አንተ አንድ የእርስዎን መሳሪያዎች እና መሳሪያዎች በፕላስቲክ መሳሪያዎች ላይ ይመልከቱ: Model model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model") ይመልከቱ ሞዴል ጥቅል እንዴት ይመልከቱ: model.summary() Model: "mnist_model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 784)] 0 dense (Dense) (None, 64) 50240 dense_1 (Dense) (None, 64) 4160 dense_2 (Dense) (None, 10) 650 ================================================================= Total params: 55050 (215.04 KB) Trainable params: 55050 (215.04 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ በተጨማሪም አንድ ግራፍ እንደ ሞዴል መፍጠር ይችላሉ: keras.utils.plot_model(model, "my_first_model.png") እና, አማራጭ, በእያንዳንዱ ጥቅል ውስጥ ውሂብ እና ውሂብ ቅርጸት በኮምፒውተር ግራፊክ ላይ ይመልከቱ: keras.utils.plot_model(model, "my_first_model_with_shape_info.png", show_shapes=True) ይህ መጠን እና ኮድ በአጠቃላይ ተመሳሳይ ናቸው. በኮድ ስሪት, የኮንክሪንግ ጫማዎች በኮምፒውተር አጠቃቀም ይቀላቀሉ. አንድ "የካሬዎች ግራፍ" አንድ ጥልቅ መግዛት ሞዴል ለ ተስማሚ ደህንነት ፎቶ ነው, እና ተግባራዊ API ይህ ተስማሚ ሞዴሎች ለመፍጠር አንድ መንገድ ነው. ልምድ, ግምገማ እና ግምገማ Training, evaluation, and inference works precisely in the same way for models built using the functional API as for ሞዴሎች Sequential The የኮምፒውተር የኮምፒውተር የኮምፒውተር የኮምፒውተር ( የክፍያ መሳሪያዎች እና የክፍያ መሳሪያዎች (የ መውሰድ: እርስዎ ቀላል ይችላሉ የክፍያ መሳሪያዎች (እንዴት: የክፍያ መሳሪያዎች) አግኙን Model fit() evaluate() እነዚህን ሉህ ያግኙ ጓንዶች እዚህ, የ MNIST ፎቶ ውሂብ ይሸፍናል, ወደ ቪክቶሮች ይመዝገቡ, የ ሞዴል በዚህ ውሂብ ላይ ይሸፍናል (በመሠረተ ስኬታማነት ላይ አፈጻጸም ማረጋገጫ ጊዜ), ከዚያም የሞዴል በ የሙከራ ውሂብ ላይ ይመዝገቡ: (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = x_train.reshape(60000, 784).astype("float32") / 255 x_test = x_test.reshape(10000, 784).astype("float32") / 255 model.compile( loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=keras.optimizers.RMSprop(), metrics=[keras.metrics.SparseCategoricalAccuracy()], ) history = model.fit(x_train, y_train, batch_size=64, epochs=2, validation_split=0.2) test_scores = model.evaluate(x_test, y_test, verbose=2) print("Test loss:", test_scores[0]) print("Test accuracy:", test_scores[1]) Epoch 1/2 750/750 [==============================] - 4s 3ms/step - loss: 0.3556 - sparse_categorical_accuracy: 0.8971 - val_loss: 0.1962 - val_sparse_categorical_accuracy: 0.9422 Epoch 2/2 750/750 [==============================] - 2s 2ms/step - loss: 0.1612 - sparse_categorical_accuracy: 0.9527 - val_loss: 0.1461 - val_sparse_categorical_accuracy: 0.9592 313/313 - 0s - loss: 0.1492 - sparse_categorical_accuracy: 0.9556 - 463ms/epoch - 1ms/step Test loss: 0.14915992319583893 Test accuracy: 0.9556000232696533 ተጨማሪ ያንብቡ, ይመልከቱ መመሪያዎች ስልጠና እና ግምገማ ያግኙ እና serialize ሞዴል ማከማቻ እና serialization በተመሳሳይ መንገድ ለ ሞዴሎች በመጠቀም የተገነባው ተግባራዊ API ጋር ይሰራሉ. ሞዴሎች. አንድ ተግባራዊ ሞዴል ለመፍጠር መደበኛ መንገድ ይደውሉ ከዚህ ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ከሁሉም ፋይሎችን ያቀርባል. Sequential model.save() ይህ የተጠበቀ ፋይል ያካትታል: ሞዴል Architecture ሞዴል ክብደት መጠን (የተወደበት ጊዜ የተመሠረተ) ሞዴል training config, if any (as passed to compile) optimizer እና በውስጡ ሁኔታ, ማንኛውም (እርስዎ የተቀየደው የትምህርት ለመጀመር) model.save("path_to_my_model.keras") del model # Recreate the exact same model purely from the file: model = keras.models.load_model("path_to_my_model.keras") ዝርዝር ለማግኘት, ሞዴል ያንብቡ መመሪያዎች serialization እና ደህንነት ተመሳሳይ ግራፊዎችን በመጠቀም ከሌሎች ሞዴሎች ለመፍጠር በ functional API ውስጥ, ሞዴሎች በ layer graph ውስጥ የእርስዎ input እና outputs ን ያካትታሉ. ይህ አንድ አንድ layer graph በመጠቀም በርካታ ሞዴሎች ለመፍጠር ሊውል ይችላል. በዚህ ምሳሌ ውስጥ, ሁለት ሞዴሎች ለማስተካከል ተመሳሳይ ጥቅሎች ይጠቀማሉ: አንድ ይህ ሞዴል በ 16 ዲሜትር ቪክቶሮች ውስጥ ምስል መተግበሪያዎችን ይፈጥራል, እና አንድ end-to-end ሞዴል የፈጠራ ሞዴሎች. encoder autoencoder encoder_input = keras.Input(shape=(28, 28, 1), name="img") x = layers.Conv2D(16, 3, activation="relu")(encoder_input) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.MaxPooling2D(3)(x) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.Conv2D(16, 3, activation="relu")(x) encoder_output = layers.GlobalMaxPooling2D()(x) encoder = keras.Model(encoder_input, encoder_output, name="encoder") encoder.summary() x = layers.Reshape((4, 4, 1))(encoder_output) x = layers.Conv2DTranspose(16, 3, activation="relu")(x) x = layers.Conv2DTranspose(32, 3, activation="relu")(x) x = layers.UpSampling2D(3)(x) x = layers.Conv2DTranspose(16, 3, activation="relu")(x) decoder_output = layers.Conv2DTranspose(1, 3, activation="relu")(x) autoencoder = keras.Model(encoder_input, decoder_output, name="autoencoder") autoencoder.summary() Model: "encoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= img (InputLayer) [(None, 28, 28, 1)] 0 conv2d (Conv2D) (None, 26, 26, 16) 160 conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 max_pooling2d (MaxPooling2 (None, 8, 8, 32) 0 D) conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 global_max_pooling2d (Glob (None, 16) 0 alMaxPooling2D) ================================================================= Total params: 18672 (72.94 KB) Trainable params: 18672 (72.94 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ Model: "autoencoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= img (InputLayer) [(None, 28, 28, 1)] 0 conv2d (Conv2D) (None, 26, 26, 16) 160 conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 max_pooling2d (MaxPooling2 (None, 8, 8, 32) 0 D) conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 global_max_pooling2d (Glob (None, 16) 0 alMaxPooling2D) reshape (Reshape) (None, 4, 4, 1) 0 conv2d_transpose (Conv2DTr (None, 6, 6, 16) 160 anspose) conv2d_transpose_1 (Conv2D (None, 8, 8, 32) 4640 Transpose) up_sampling2d (UpSampling2 (None, 24, 24, 32) 0 D) conv2d_transpose_2 (Conv2D (None, 26, 26, 16) 4624 Transpose) conv2d_transpose_3 (Conv2D (None, 28, 28, 1) 145 Transpose) ================================================================= Total params: 28241 (110.32 KB) Trainable params: 28241 (110.32 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ እዚህ, decoding መዋቅር በ coding መዋቅር ጋር ጥንካሬ symmetrical ነው, ስለዚህ output ቅርጸት input ቅርጸት ጋር ተመሳሳይ ነው . (28, 28, 1) ከ A ከ A ከ አግኝቷል a አግኝቷል, እና በ A አግኝቷል አግኝቷል Conv2D Conv2DTranspose MaxPooling2D UpSampling2D ሁሉም ሞዴሎች እንደ layers እንደ callable ናቸው እርስዎ ማንኛውም ሞዴል እርስዎ አንድ ቅርንጫፍ እንደ መጠቀም ይችላሉ. አንድ ሞዴል ይደውሉ, አንተ ብቻ ሞዴል መዋቅር መጠቀም አይችልም, አንተ ደግሞ በውስጡ ክብደት መጠቀም ይሆናል. Input ይህ መተግበሪያ ውስጥ ለማየት, አንድ የ autoencoder ሞዴል, አንድ decoder ሞዴል ለመፍጠር እና በ autoencoder ሞዴል ለማግኘት በሁለት ጥቅሞች ውስጥ ያካትታል በ autoencoder ሞዴል ላይ አንድ የተለያዩ መተግበሪያ ይመልከቱ: encoder_input = keras.Input(shape=(28, 28, 1), name="original_img") x = layers.Conv2D(16, 3, activation="relu")(encoder_input) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.MaxPooling2D(3)(x) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.Conv2D(16, 3, activation="relu")(x) encoder_output = layers.GlobalMaxPooling2D()(x) encoder = keras.Model(encoder_input, encoder_output, name="encoder") encoder.summary() decoder_input = keras.Input(shape=(16,), name="encoded_img") x = layers.Reshape((4, 4, 1))(decoder_input) x = layers.Conv2DTranspose(16, 3, activation="relu")(x) x = layers.Conv2DTranspose(32, 3, activation="relu")(x) x = layers.UpSampling2D(3)(x) x = layers.Conv2DTranspose(16, 3, activation="relu")(x) decoder_output = layers.Conv2DTranspose(1, 3, activation="relu")(x) decoder = keras.Model(decoder_input, decoder_output, name="decoder") decoder.summary() autoencoder_input = keras.Input(shape=(28, 28, 1), name="img") encoded_img = encoder(autoencoder_input) decoded_img = decoder(encoded_img) autoencoder = keras.Model(autoencoder_input, decoded_img, name="autoencoder") autoencoder.summary() Model: "encoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= original_img (InputLayer) [(None, 28, 28, 1)] 0 conv2d_4 (Conv2D) (None, 26, 26, 16) 160 conv2d_5 (Conv2D) (None, 24, 24, 32) 4640 max_pooling2d_1 (MaxPoolin (None, 8, 8, 32) 0 g2D) conv2d_6 (Conv2D) (None, 6, 6, 32) 9248 conv2d_7 (Conv2D) (None, 4, 4, 16) 4624 global_max_pooling2d_1 (Gl (None, 16) 0 obalMaxPooling2D) ================================================================= Total params: 18672 (72.94 KB) Trainable params: 18672 (72.94 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ Model: "decoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= encoded_img (InputLayer) [(None, 16)] 0 reshape_1 (Reshape) (None, 4, 4, 1) 0 conv2d_transpose_4 (Conv2D (None, 6, 6, 16) 160 Transpose) conv2d_transpose_5 (Conv2D (None, 8, 8, 32) 4640 Transpose) up_sampling2d_1 (UpSamplin (None, 24, 24, 32) 0 g2D) conv2d_transpose_6 (Conv2D (None, 26, 26, 16) 4624 Transpose) conv2d_transpose_7 (Conv2D (None, 28, 28, 1) 145 Transpose) ================================================================= Total params: 9569 (37.38 KB) Trainable params: 9569 (37.38 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ Model: "autoencoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= img (InputLayer) [(None, 28, 28, 1)] 0 encoder (Functional) (None, 16) 18672 decoder (Functional) (None, 28, 28, 1) 9569 ================================================================= Total params: 28241 (110.32 KB) Trainable params: 28241 (110.32 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ እርስዎ መውሰድ ይችላሉ እንደ, ሞዴል ሊሆን ይችላል: አንድ ሞዴል አዲሱ ሞዴሎች ሊሆን ይችላል (አንድ ሞዴል እንደ አንድ ጥቅል ነው). ለምሳሌ, አንድ ሞዴሎች ስብስቦችን አንድ ሞዴል ውስጥ እንዴት ያካትታል, ይህም የእነርሱ ጥቅሞችን አነስተኛ ያካትታል: አጠቃቀም def get_model(): inputs = keras.Input(shape=(128,)) outputs = layers.Dense(1)(inputs) return keras.Model(inputs, outputs) model1 = get_model() model2 = get_model() model3 = get_model() inputs = keras.Input(shape=(128,)) y1 = model1(inputs) y2 = model2(inputs) y3 = model3(inputs) outputs = layers.average([y1, y2, y3]) ensemble_model = keras.Model(inputs=inputs, outputs=outputs) የኮምፒውተር የኮምፒውተር የኮምፒውተር ብዙ input እና outputs ጋር ሞዴሎች የፕሮግራም መተግበሪያ (API) በይፋ መተግበሪያዎች እና መተግበሪያዎች ይጠቀማል. ይህ መተግበሪያ በይፋ መተግበሪያዎች ጋር መጠቀም አይችልም. አግኝቷል Sequential ለምሳሌ, እርስዎ ደንበኞች ልውውጥ ቲኬቶች በመጀመሪያ ደረጃ ለማግኘት እና እነርሱን ትክክለኛ ክፍሎች ወደ ለመርዳት አንድ ሥርዓት ለመፍጠር ይችላሉ, ከዚያም ሞዴል የሦስት መተግበሪያዎች ይኖራሉ: የምስክር ወረቀት ስም (ኮምፒውተር መግቢያ) የፕላስቲክ ምልክቶች (የፕላስቲክ ምልክቶች) እና ማንኛውም መለያዎች ተጠቃሚው ያካትታል (kategorical input) ይህ ሞዴል ሁለት ውፅዓት ይሆናል: የ 0 እና 1 (Scalar Sigmoid Output) መካከል የ prioritet ጥቅል, እና የፕሮጀክት ማረፊያ ማረፊያ (Softmax output over the set of departments) ይህ ሞዴል በ Functional API ጋር አንዳንድ መስኮች ውስጥ ለመፍጠር ይችላሉ: num_tags = 12 # Number of unique issue tags num_words = 10000 # Size of vocabulary obtained when preprocessing text data num_departments = 4 # Number of departments for predictions title_input = keras.Input( shape=(None,), name="title" ) # Variable-length sequence of ints body_input = keras.Input(shape=(None,), name="body") # Variable-length sequence of ints tags_input = keras.Input( shape=(num_tags,), name="tags" ) # Binary vectors of size `num_tags` # Embed each word in the title into a 64-dimensional vector title_features = layers.Embedding(num_words, 64)(title_input) # Embed each word in the text into a 64-dimensional vector body_features = layers.Embedding(num_words, 64)(body_input) # Reduce sequence of embedded words in the title into a single 128-dimensional vector title_features = layers.LSTM(128)(title_features) # Reduce sequence of embedded words in the body into a single 32-dimensional vector body_features = layers.LSTM(32)(body_features) # Merge all available features into a single large vector via concatenation x = layers.concatenate([title_features, body_features, tags_input]) # Stick a logistic regression for priority prediction on top of the features priority_pred = layers.Dense(1, name="priority")(x) # Stick a department classifier on top of the features department_pred = layers.Dense(num_departments, name="department")(x) # Instantiate an end-to-end model predicting both priority and department model = keras.Model( inputs=[title_input, body_input, tags_input], outputs=[priority_pred, department_pred], ) አሁን ሞዴል ይጫኑ: keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True) ይህ ሞዴል ማዘጋጀት ጊዜ, እያንዳንዱ ውፅዓት የተለያዩ ውፅዓት መሳሰሉ ይችላሉ. እያንዳንዱ ውፅዓት ወደ የተለያዩ ውፅዓት መሳሰሉ ይችላሉ. model.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss=[ keras.losses.BinaryCrossentropy(from_logits=True), keras.losses.CategoricalCrossentropy(from_logits=True), ], loss_weights=[1.0, 0.2], ) የ output layers የተለያዩ ስም አላቸው, እርስዎ ደግሞ ተስማሚ layer names ጋር የሙቀት እና የሙቀት ክብደት ያካትታል: model.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss={ "priority": keras.losses.BinaryCrossentropy(from_logits=True), "department": keras.losses.CategoricalCrossentropy(from_logits=True), }, loss_weights={"priority": 1.0, "department": 0.2}, ) በ NumPy መተግበሪያዎች እና መተግበሪያዎች መካከል ዝርዝርዎችን በመውሰድ ወደ ሞዴል ያግኙ: # Dummy input data title_data = np.random.randint(num_words, size=(1280, 10)) body_data = np.random.randint(num_words, size=(1280, 100)) tags_data = np.random.randint(2, size=(1280, num_tags)).astype("float32") # Dummy target data priority_targets = np.random.random(size=(1280, 1)) dept_targets = np.random.randint(2, size=(1280, num_departments)) model.fit( {"title": title_data, "body": body_data, "tags": tags_data}, {"priority": priority_targets, "department": dept_targets}, epochs=2, batch_size=32, ) Epoch 1/2 40/40 [==============================] - 8s 112ms/step - loss: 1.2982 - priority_loss: 0.6991 - department_loss: 2.9958 Epoch 2/2 40/40 [==============================] - 3s 64ms/step - loss: 1.3110 - priority_loss: 0.6977 - department_loss: 3.0666 አግኙን ይደውሉ ፡፡ እርስዎ እንደ አንድ ፎቶ መጻፍ ይችላሉ. ነገር ግን እንደ አንድ የኮምፒውተር . Dataset ([title_data, body_data, tags_data], [priority_targets, dept_targets]) ({'title': title_data, 'body': body_data, 'tags': tags_data}, {'priority': priority_targets, 'department': dept_targets}) ተጨማሪ ዝርዝሮች ለማግኘት, በ መመሪያዎች ስልጠና እና ግምገማ አንድ ተስማሚ ሞዴል ከብዙ መተግበሪያዎች እና ውሂብዎች ጋር ሞዴሎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ከብዙ መተግበሪያዎች ናቸው. የፋይበር መጠቀም አይችልም. Sequential ለ CIFAR10 አንድ ተጫዋች ResNet ሞዴል ለመፍጠር ይህን ማረጋገጥ ይሆናል: inputs = keras.Input(shape=(32, 32, 3), name="img") x = layers.Conv2D(32, 3, activation="relu")(inputs) x = layers.Conv2D(64, 3, activation="relu")(x) block_1_output = layers.MaxPooling2D(3)(x) x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_1_output) x = layers.Conv2D(64, 3, activation="relu", padding="same")(x) block_2_output = layers.add([x, block_1_output]) x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_2_output) x = layers.Conv2D(64, 3, activation="relu", padding="same")(x) block_3_output = layers.add([x, block_2_output]) x = layers.Conv2D(64, 3, activation="relu")(block_3_output) x = layers.GlobalAveragePooling2D()(x) x = layers.Dense(256, activation="relu")(x) x = layers.Dropout(0.5)(x) outputs = layers.Dense(10)(x) model = keras.Model(inputs, outputs, name="toy_resnet") model.summary() Model: "toy_resnet" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== img (InputLayer) [(None, 32, 32, 3)] 0 [] conv2d_8 (Conv2D) (None, 30, 30, 32) 896 ['img[0][0]'] conv2d_9 (Conv2D) (None, 28, 28, 64) 18496 ['conv2d_8[0][0]'] max_pooling2d_2 (MaxPoolin (None, 9, 9, 64) 0 ['conv2d_9[0][0]'] g2D) conv2d_10 (Conv2D) (None, 9, 9, 64) 36928 ['max_pooling2d_2[0][0]'] conv2d_11 (Conv2D) (None, 9, 9, 64) 36928 ['conv2d_10[0][0]'] add (Add) (None, 9, 9, 64) 0 ['conv2d_11[0][0]', 'max_pooling2d_2[0][0]'] conv2d_12 (Conv2D) (None, 9, 9, 64) 36928 ['add[0][0]'] conv2d_13 (Conv2D) (None, 9, 9, 64) 36928 ['conv2d_12[0][0]'] add_1 (Add) (None, 9, 9, 64) 0 ['conv2d_13[0][0]', 'add[0][0]'] conv2d_14 (Conv2D) (None, 7, 7, 64) 36928 ['add_1[0][0]'] global_average_pooling2d ( (None, 64) 0 ['conv2d_14[0][0]'] GlobalAveragePooling2D) dense_6 (Dense) (None, 256) 16640 ['global_average_pooling2d[0][ 0]'] dropout (Dropout) (None, 256) 0 ['dense_6[0][0]'] dense_7 (Dense) (None, 10) 2570 ['dropout[0][0]'] ================================================================================================== Total params: 223242 (872.04 KB) Trainable params: 223242 (872.04 KB) Non-trainable params: 0 (0.00 Byte) __________________________________________________________________________________________________ ወደ ሞዴል ያውቃል: keras.utils.plot_model(model, "mini_resnet.png", show_shapes=True) አሁን ሞዴል መውሰድ: (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data() x_train = x_train.astype("float32") / 255.0 x_test = x_test.astype("float32") / 255.0 y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) model.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss=keras.losses.CategoricalCrossentropy(from_logits=True), metrics=["acc"], ) # We restrict the data to the first 1000 samples so as to limit execution time # on Colab. Try to train on the entire dataset until convergence! model.fit(x_train[:1000], y_train[:1000], batch_size=64, epochs=1, validation_split=0.2) 13/13 [==============================] - 4s 39ms/step - loss: 2.3086 - acc: 0.0988 - val_loss: 2.3020 - val_acc: 0.0850 በመጀመሪያ በ TensorFlow ድር ጣቢያ ላይ የተለጠፈው, ይህ ጽሑፍ እዚህ አንድ አዲስ ምልክት ውስጥ ይመልከቱ እና በ CC BY 4.0. ኮድ ናሙናዎች በ Apache 2.0 License መሠረት ይሰጣል. በመጀመሪያ በ TensorFlow ድር ጣቢያ ላይ የተለጠፈው, ይህ ጽሑፍ እዚህ አንድ አዲስ ምልክት ውስጥ ይመልከቱ እና በ CC BY 4.0. ኮድ ናሙናዎች በ Apache 2.0 License መሠረት ይሰጣል. የ TensorFlow