{
  "id": 376861,
  "title": "model.fit Error",
  "url": "/competitions/siim-isic-melanoma-classification/discussion/376861",
  "author_name": "Amiru Abdulkarim",
  "post_date": "2023-01-08T22:11:55.802000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I am stuck at model.fit, its returning error.<br>\nplease see my code: I have also tried model.fit_generator but it didn't work</p>\n<p>model = tf.keras.Sequential([<br>\n    tf.keras.layers.Flatten(input_shape=(150,150)),<br>\n    tf.keras.layers.Dense(20, activation='relu'),<br>\n    tf.keras.layers.Dense(20)<br>\n    ])</p>\n<p>model.compile(optimizer='adam',<br>\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),<br>\n              metrics=['accuracy'])</p>\n<p>model.fit(training_set,<br>\n    validation_data = test_set,<br>\n    epochs = 10,<br>\n    steps_per_epoch = len(training_set),<br>\n    validation_steps = len(test_set)<br>\n)</p>",
  "messages": [
    {
      "id": 2091958,
      "postDate": "2023-01-08T22:11:55.803Z",
      "content": "<p>I am stuck at model.fit, its returning error.<br>\nplease see my code: I have also tried model.fit_generator but it didn't work</p>\n<p>model = tf.keras.Sequential([<br>\n    tf.keras.layers.Flatten(input_shape=(150,150)),<br>\n    tf.keras.layers.Dense(20, activation='relu'),<br>\n    tf.keras.layers.Dense(20)<br>\n    ])</p>\n<p>model.compile(optimizer='adam',<br>\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),<br>\n              metrics=['accuracy'])</p>\n<p>model.fit(training_set,<br>\n    validation_data = test_set,<br>\n    epochs = 10,<br>\n    steps_per_epoch = len(training_set),<br>\n    validation_steps = len(test_set)<br>\n)</p>",
      "rawMarkdown": "I am stuck at model.fit, its returning error.\nplease see my code: I have also tried model.fit_generator but it didn't work\n\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Flatten(input_shape=(150,150)),\n    tf.keras.layers.Dense(20, activation='relu'),\n    tf.keras.layers.Dense(20)\n    ])\n\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\nmodel.fit(training_set,\n    validation_data = test_set,\n    epochs = 10,\n    steps_per_epoch = len(training_set),\n    validation_steps = len(test_set)\n)"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2091958": "I am stuck at model.fit, its returning error.\nplease see my code: I have also tried model.fit_generator but it didn't work\n\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Flatten(input_shape=(150,150)),\n    tf.keras.layers.Dense(20, activation='relu'),\n    tf.keras.layers.Dense(20)\n    ])\n\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\nmodel.fit(training_set,\n    validation_data = test_set,\n    epochs = 10,\n    steps_per_epoch = len(training_set),\n    validation_steps = len(test_set)\n)"
  }
}